# Contracts in the Age of Smart Readers

Canonical page: https://works.battleoftheforms.com/papers/ssrn-3740356/

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Contracts in the Age of Smart Readers
Yonathan A. Arbel* & Shmuel I. Becher**
ABSTRACT
What does it mean to have machines that can read, explain, and evaluate
contracts? Recent advances in machine learning have led to a fundamental
breakthrough in machine language models, portending a profound shift in the
ability of machines to process text. Such a shift has far-reaching consequences
for diverse areas of law, which are predicated on, and justified by, the existence of information barriers. Our object here is to provide a general framework for evaluating the legal and policy implications of employing language
models as “smart readers”—tools that read, analyze, and assess contracts, disclosures, and privacy policies.
Synthesizing state-of-the-art developments, we identify four core capabilities of smart readers. Based on real-world examples produced by new machine-learning models, we demonstrate that smart readers can: simplify
complex legal language; personalize the contractual presentation to the user’s
specific sociocultural identity; interpret the meaning of contractual terms; and
benchmark and rank contracts based on their quality.
Nevertheless, the implications of smart readers are more complex than
initially meets the eye. Although smart readers can overcome traditional information barriers and empower parties, they rely on black-box models that sophisticated parties can exploit. Smart readers can close some of the gaps in
access to justice, but they also introduce concerns about contractual bias and
discrimination. And even though smart readers can improve term transparency, they might lead judges and policymakers to relax their guard
prematurely.
The current body of doctrine and scholarship is ill equipped to address
both the prospects and risks of smart reader technology. This Article narrows
this gap. It maps the necessary theoretical, policy, and doctrinal adaptations to
the age when machines can automate the reading of contracts.
* Associate Professor, University of Alabama School of Law.
** Professor of Law, Victoria University of Wellington; J.S.D., LL.M., Yale University.
We are grateful for helpful comments and suggestions made by Ben Alarie, Chris Bradley,
Matt Bruckner, Tony Casey, Shahar Dillbary, Meirav Furth-Matzkin, Gwern Branwen, Dave
Hoffman, Joasia Luzak, Rory Van Loo, Tess Wilkinson-Ryan, Eyal Zamir, Stephan Stolz, and
Tal Zarsky, as well as to participants in the AALS Section on Contracts (2021), the Law of
Consumer Markets Seminar at Boston University (2021), the Australasian Consumer Law
Roundtable (2020), the Exeter Law School: The Centre for European Legal Studies (2020), and
the Haifa University Faculty Workshop (2021). We appreciate the outstanding research assistance provided by Marcus Armband, William Brand, and William Britton.
February 2022 Vol. 90 No. 1

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TABLE OF CONTENTS
INTRODUCTION................................................. 84 R
I. SMART READERS: TECHNOLOGY AND CAPABILITIES.... 94 R
A. Simplification ....................................... 95 R
B. Personalization...................................... 99 R
C. Construction ........................................ 104 R
D. Benchmarking ...................................... 106 R
II. SMART READER UPTAKE AND (NO)
READING THEORIES .................................... 109 R
III. SMART READERS: IMPLICATIONS........................ 114 R
A. Matching, Search Costs, and Market Competition ... 115 R
B. Errors and Adversarial Attacks ..................... 118 R
C. Access to Justice .................................... 124 R
D. Compliance and Overcompliance ................... 126 R
E. Discrimination and Personalization ................. 127 R
F. Nudging with Smart Readers........................ 131 R
IV. REGULATING CONTRACTS IN THE AGE OF
SMART READERS ....................................... 133 R
A. The Challenge to Consumer Protection.............. 133 R
B. Courts and Agencies ................................ 136 R
C. Regulatory and Doctrinal Responses ................ 137 R
1. Allocation of Error Costs....................... 137 R
2. The Duty to Read .............................. 140 R
3. The Problem of Adversarial Attacks............ 141 R
4. Bias and Discrimination ........................ 143 R
CONCLUSION ................................................... 146 R
INTRODUCTION
Consider an individual who is about to purchase a tablet device.
Tucked inside the boilerplate is the following clause:

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12. Controlling Law and Severability. This License will be governed by and construed in accordance with the laws of the State
of California, excluding its conflict of law principles. This License shall not be governed by the United Nations Convention
on Contracts for the International Sale of Goods, the application of which is expressly excluded. If you are a consumer based
in the United Kingdom, this License will be governed by the
laws of the jurisdiction of your residence. If for any reason a
court of competent jurisdiction finds any provision, or portion
thereof, to be unenforceable, the remainder of this License shall
continue in full force and effect.1
There are good reasons for the individual—a buyer, an employee,
a tenant, or a lessee—to care which law governs the transaction, as it
affects their procedural and substantive rights.2 However, reading the
boiler plate is cognitively taxing, emotionally draining, and time intensive.3 Moreover, reading is not enough: one also needs to understand.
What does “controlling law” mean? What is “severability”? Does it
matter that California law governs the contract?
The typical response of many individuals to these challenges is
simple: ignore the text altogether.4 Such a response, however, undermines the meaning of informed consent. Moreover, anticipating this
response, firms may strategically insert one-sided clauses and potentially add bloat and complexity to their contracts to further discourage
1 APPLE, SOFTWARE LICENSE AGREEMENTS: SINGLE USE LICENSE ¶ 12, https://
www.apple.com/legal/sla/docs/iOS112.pdf [https://perma.cc/S7J7-C323].
2 States differ greatly in the quality of their bundle of consumer protection laws and some
consumer organizations rank them. See, e.g., CAROLYN CARTER, NAT’L CONSUMER L. CTR.,
CONSUMER PROTECTION IN THE STATES (2018), https://www.nclc.org/images/pdf/udap/udap-report.pdf [https://perma.cc/L63Y-DMQV].
3 See, e.g., Melvin Aron Eisenberg, Text Anxiety, 59 S. CAL. L. REV. 305, 309 (1986)
(arguing that consumers find reading dense texts of form contracts a daunting task); Robert A.
Hillman & Jeffrey J. Rachlinski, Standard-Form Contracting in the Electronic Age, 77 N.Y.U. L.
REV. 429, 436 (2002) (highlighting “the costs of reading, interpreting, and comparing standard
terms”).
4 See, e.g., Yannis Bakos, Florencia Marotta-Wurgler & David R. Trossen, Does Anyone
Read the Fine Print? Consumer Attention to Standard-Form Contracts, 43 J. LEGAL STUD. 1, 3
(2014) (finding that consumers rarely read end-user license agreements); see also Ian Ayres &
Alan Schwartz, The No-Reading Problem in Consumer Contract Law, 66 STAN. L. REV. 545, 546
(2014) (“People rarely read the forest of trees that are harvested and mailed in the form of credit
card and cell phone contracts, insurance policies, gym membership agreements, or mutual fund
prospectuses.”); RESTATEMENTOF CONSUMER CONTS. §3 reporters’ notes, at 63 (AM. L. INST.,
Tentative Draft 2019) [hereinafter DRAFT RESTATEMENT 2019] (“The standard contract terms
are invisible to most consumers . . . .”).

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reading.5 Courts, legislators, and agencies are trying to hedge some of
the negative results of this dynamic but have had limited success.6
This dismal equilibrium is now facing disruption. Advances in
language models—a branch of artificial intelligence (“AI”)—have
given rise to a novel technology: “smart readers.”7 Using a smart
reader, a prospective buyer can pull out her phone, scan the clause
above, and click “explain.” The smart reader offers this succinct
summary:8
The human-machine interaction, however, does not need to end
here. If the specific user prefers the use of concrete examples rather
than abstract statements, she can click “example”:9
5 See, e.g., Melvin Aron Eisenberg, The Limits of Cognition and the Limits of Contract, 47
STAN. L. REV. 211, 241 (1995) (“Form insurance contracts, for example, typically include thirty,
forty, or more terms. Moreover, the meaning and effect of the preprinted provisions will very
often be inaccessible to laypersons.”); Russell Korobkin, Bounded Rationality, Standard Form
Contracts, and Unconscionability, 70 U. CHI. L. REV. 1203, 1239–44 (2003) (explaining that firms
will “race to the bottom” with respect to the quality of nonsalient contract terms); see also Stephen J. Choi, Mitu Gulati & Robert E. Scott, Variation in Boilerplate: Rational Design or Random Mutation?, 20 AM. L. & ECON. REV. 1 (2018) (documenting the existence of inertia and
encrustation of legal terms in commercial contracts); Lauren E. Willis, Performance-Based Consumer Law, 82 U. CHI. L. REV. 1309, 1317–21 (2015) (exploring strategic manipulations of contractual text). But see David A. Hoffman, Relational Contracts of Adhesion, 85 U. CHI. L. REV.
1395, 1421–41 (2018) (investigating instances where firms deliberately create consumer-friendly
contracts).
6 See, e.g., MARGARET JANE RADIN, BOILERPLATE: THE FINE PRINT, VANISHING RIGHTS,
ANDTHE RULEOF LAW 8–12 (2013) (arguing that consumer contracts erode consumer rights and
allow firms to create their own legal universe); W. David Slawson, Standard Form Contracts and
Democratic Control of Lawmaking Power, 84 HARV. L. REV. 529, 529 (1971) (submitting that
consumer contract terms are “almost universally unfair”); see also Jean Braucher, Unfair Terms
in Comparative Perspective: Software Contracts, inCOMMERCIAL CONTRACT LAW: TRANSATLANTIC PERSPECTIVES 339, 339 (Larry A. DiMatteo et al. eds., 2013) (“[M]ost policymakers, regulators, and scholars concede that there often can be no real assent to mass-market standard terms,
but then balk at meaningful solutions to address market failure.”); Ethan J. Leib, What is the
Relational Theory of Consumer Form Contract?, inREVISITINGTHE CONTRACTS SCHOLARSHIP
OF STEWART MACAULAY 259, 259 (Jean Braucher et al. eds., 2013) (“One of the most puzzling
and embarrassing facts about contract law and contracts scholarship in the United States is that
neither has found a consistent way to treat the real contracts of our lives: standardised consumer
form contracts.”).
7 See infra Part I for a discussion of the technology.
8 Screenshot of smart reader explanation [1] (on file with authors).
9 Screenshot of smart reader explanation [1] (on file with authors).

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With these clarifications in hand, the user now wants to understand the meaning of “severability.” She clicks on the term, and the
smart reader responds:10
Feeling that she has a sufficient grasp of the contract, the user
now wants to know more about the accompanying privacy policy. She
clicks “benchmark”:11
The score allows her to assess the overall strength of the policy in
a glimpse. Critically, the smart reader also offers an industry mean
score and comparisons to competitors who offer better terms. Taken
together, the smart reader not only provides an understanding of the
fine print but also of the market and the alternatives it offers.
To better see the practical import of smart readers, consider the
textbook staple of Williams v. Walker-Thomas Furniture Co.12 Ms.
Williams, a mother of seven living on social benefits, entered a rent10 Content produced via https://play.aidungeon.io/main/home [https://perma.cc/DV9QS47L].
11 PrivacyCheck: Overview, CHROME WEB STORE, https://chrome.google.com/webstore/
detail/privacycheck/poobeppenopkcbjejfjenbiepifcbclg/related?hl=en]-US [https://perma.cc/
8KVM-3HTE].
12 350 F.2d 445 (D.C. Cir. 1965).

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to-own agreement for a stereo set.13 The agreement stated that the
store held title to goods sold until paid in full.14 It also contained the
following clause, described by the court as “rather obscure”:15
[T]he amount of each periodical installment payment to be
made by [purchaser] to the Company under this present lease
shall be inclusive of and not in addition to the amount of each
installment payment to be made by [purchaser] under such prior leases, bills or accounts; and all payments now and hereafter
made by [purchaser] shall be credited pro rata on all outstanding leases, bills and accounts due the Company by [purchaser]
at the time each such payment is made.
If Ms. Williams had a smart reader on her phone,16 she could
have tapped it to receive the following output:17
This output marks the consequences of the cross-collateral agreement in relatively simple terms. Although it is not perfect—and does
not make the term any less one-sided—it marks a distinct improvement over the original language. Empowered by a better understanding of the transaction, shoppers may search for a better deal in
another store or avoid the purchase altogether.18
13 Id. at 447–48.
14 Id. at 447.
15 Id. (clarifying that the meaning “of this rather obscure provision was to keep a balance
due on every item purchased until the balance due on all items, whenever purchased, was liquidated”); see also Tess Wilkinson-Ryan, A Psychological Account of Consent to Fine Print, 99
IOWA L. REV. 1745, 1759 (2014) (describing the term as “so opaque that it would be unreasonable to expect parties without advanced education to understand the financial risk”).
16 For a discussion of the availability of smart phones among low-income individuals, see
infra note 170 and accompanying text. R
17 Screenshot of smart reader explanation [2] (on file with authors).
18 See Russell Korobkin, A “Traditional” and “Behavioral” Law-and-Economics Analysis
of Williams v. Walker-Thomas Furniture Company, 26 U. HAW. L. REV. 441, 452 (2004) (“There
is no reason to believe that Williams lacked the choice of shopping elsewhere.”). In infra Section
III.A we develop the point that even in markets where choice is limited, empowering consumers
can have dynamic competitive effects.

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We did not write these examples, nor did any other human, for
that matter. Rather, the examples are taken from a recently released
version of a language model called GPT-3.19 Remarkably, we used a
weak version of this model, and we did not use fine-tuning or optimization. The only caveat—and one to keep in mind throughout the
Article—is that we cherry-picked the examples.20 Nevertheless, an
app’s ability to respond intelligently to queries about an unfamiliar
legal text is a clear technological breakthrough.
This Article aims to analyze the capabilities of smart readers,
evaluate their significance for consumer and contract law, and illuminate some of the hidden benefits and risks they carry. 21 In the process, it joins contemporary conversations in law and technology by
illuminating several key questions: Can the reading of disclosures, privacy policies, and consumer contracts be automated? What does the
growing transparency in agreements mean for markets, firms, and individuals? Can we think of assent as a technological challenge rather
than an ethical one? What does consumer adoption of the technology
tell us about our theories of consumer behavior? And what remains of
the case for pro-consumer regulation if reading is automated?
We investigate these questions in four Parts. Part I offers a comprehensive analysis of smart readers’ capabilities. Using concrete examples, smart readers are shown to be effective in the
(1) simplification and summary of the text; (2) personalization of text
to the specific readers’ characteristics; (3) construction of the meaning
of the contract; and (4) benchmarking of contracts by assigning them a
score relative to the competition. To be sure, these high-tech capabilities have their low-tech counterparts. Lawyers and consultants would
gladly perform these services for their clients, but the difference is in
the cost, speed, and accessibility.22 Between a lawyer and a
smartphone, only the latter fits in the pocket.
With any technology, a critical question is whether individuals
will choose to use it, and it cannot be merely assumed that adoption
will be broad, swift, or inclusive. Part II grapples with this question,
19 For more information on GPT-3, see infra notes 39–45 and accompanying text. R
20 See infra Section III.B.
21 Our analysis complements a separate technological development, that of AI assistants—
such as Alexa or Siri—who execute transactions on behalf of the consumer. The development of
assistants and their market implications were analyzed at depth in Rory Van Loo, Digital Market
Perfection, 117 MICH. L. REV. 815 (2019).
22 See RONALD L. BURDGE, UNITED STATES CONSUMER LAW: ATTORNEY FEE SURVEY
REPORT 2017–2018, at 26 (2019) (claiming that “the average hourly rate for the typical Consumer Law attorney in the United States is $345”).

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with mindful awareness of the notorious track record of many predictions on technological adoption.23 On the one hand, there is a common intuition that consumers are averse to reading, in any form. On
this intuition, even if smart readers prove effective and affordable, uptake would be limited. On the other hand, users have shown deep
interest in technologies that facilitate transactional information: consumers quickly adapted to online reviews, which they voraciously consume.24 Moreover, smart readers have already proven their mettle in
the field: sophisticated hedge funds have unleashed their proprietary
smart readers on firm disclosures and entrusted them with making
trading decisions—in the billions of dollars.25 These and other considerations discussed in Part II demonstrate that the technology is sufficiently mature to warrant serious attention today.
That said, the prospect that smart readers might fail is intriguing
in and of itself. Invoking the notion of Wittgenstein’s Ruler,26 we propose that lukewarm adoption should invite deep reflection on the validity of theories that set to explain reading gaps. After all, many of
our theories are anchored in the semantic complexity, length, and
formatting of the form—and smart readers address these issues directly. Most provocatively, lax demand may suggest that average consumers do not share the sentiment of some commentators, in that they
23 See, e.g., The 22 Worst Tech Predictions of All Time, HERO LABS (Aug. 1, 2019), https://
www.hero-labs.com/blog/the-22-worst-tech-predictions-of-all-time/ [https://perma.cc/UZ6MX8GZ] (“‘The automobile is a fad, a novelty. Horses are here to stay.’ . . . ‘Remote shopping,
while entirely feasible, will certainly flop. It has no chance of success.’ Time Magazine,
1966. . . . ‘I don’t know . . . there just aren’t that many videos I want to watch.’ Steve Chen,
founder of YouTube . . . 2005. . . . ‘There is no chance of the iPhone ever gaining significant
market share’. Steve Ballmer, CEO of Microsoft, 2007.”); see also Van Loo, supra note 21 (high-R
lighting the difficulty and need to craft legal responses for early-stage technologies).
24 See Yonathan A. Arbel, Reputation Failure: The Limits of Market Discipline in Consumer Markets, 54 WAKE FOREST L. REV. 1239, 1289 (2019) (noting the history and rapid acceptance of online reviews). As noted there, consumers not only read star averages, but also written
reviews. This demonstrates that consumers have at least some appetite for learning about transactions through reading.
25 Sean Cao, Wei Jiang, Baozhong Yang & Alan L. Zhang, How to Talk when a Machine Is
Listening: Corporate Disclosure in the Age of AI 1 (Nat’l Bureau of Econ. Rsch., Working Paper
No. 27950, 2020) (“A substantial amount of buying and selling of shares are triggered by recommendations made by robots and algorithms which process information with machine learning
tools and natural language processing kits.”); Adam Satariano & Nishant Kumar, The Massive
Hedge Fund Betting on AI, BLOOMBERG (Sept. 27, 2017, 12:00 AM), https://www.bloomberg.
com/news/features/2017-09-27/the-massive-hedge-fund-betting-on-ai [https://perma.cc/8CZ4VVYU].
26 See LUDWIG WITTGENSTEIN, REMARKSONTHE FOUNDATIONSOF MATHEMATICS 21–28
(G.H. Von Wright et al. eds., G.E.M. Anscombe trans., rev. ed. 1978) (“Am I always measuring
the table; am I not sometimes checking the ruler?”); infra note 102 and accompanying text. R

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do not feel the risk of boilerplate is similar to “lay[ing one’s] head into
the mouth of a lion.”27
Part III examines the social implications of smart readers. Even
with limited adoption, smart readers can have broad market effects,
plausibly jumpstarting term competition in dormant markets. But
along with their many salutary effects, smart readers also carry significant risks. These include discrimination, bias, and errors—risks that
further merit taking this technology seriously. One novel concern we
address is that of adversarial attacks by sophisticated firms.28 Adversarial attacks are a growing concern among computer scientists, who
define them as the intentional insertion of “malicious inputs modified
to yield erroneous model outputs.”29 The effects of these attacks are
far reaching and will become a pressing concern in many areas where
AI-based models are deployed.
Finally, Part IV examines the legal implications of smart readers.
Many consumer protection measures use the lack of reading and understanding of contracts as their fulcrum.30 For example, in an important recent article, Robin Bradley Kar and Margaret Jane Radin
argued that boilerplate aspects of a transaction are “no longer contract” because they drive a wedge between the parties’ shared understanding, inviting deception.31 Indeed, reading and understanding
27 KARL N. LLEWELLYN, THE COMMON LAW TRADITION: DECIDING APPEALS 362–71
(1960).
28 The closest discussion we are aware of is Van Loo, supra note 21, at 841–43, who consid-R
ers the possibility that sellers will change product attributes strategically to make comparison
shopping harder for AI agents. Even outside of contract law, the concept is rarely addressed.
The only other legal articles known to us that deal with adversarial examples are Gary Marchant
& Rida Bazzi, Autonomous Vehicles and Liability: What Will Juries Do?, 26 B.U. J. SCI. & TECH.
L. 67, 76 (2020) (mentioning adversarial examples in passing); and Andrew D. Selbst, Negligence
and AI’s Human Users, 100 B.U. L. REV. 1315, 1350–54 (2020) (explaining the challenge posed
by adversarial attacks to conventional tort law).
29 Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik &
Ananthram Swami, Practical Black-Box Attacks Against Machine Learning, in PROCEEDINGSOF
THE 2017 ACM ASIA CONFERENCEON COMPUTERAND COMMUNICATIONS SECURITY506, 506
(2017), https://dl.acm.org/doi/pdf/10.1145/3052973.3053009 [https://perma.cc/X485-4DPA].
30 See Ayres & Schwartz, supra note 4, at 549 (“Consumer protection law responds to the R
doctrine by attempting to induce firms to create a real opportunity for consumers to read.”); see
also DRAFT RESTATEMENT 2019, supra note 4, at 1 (“Consumer contracts present a fundamental R
challenge . . . arising from the asymmetry in information, sophistication, and stakes between the
parties . . . .”).
31 Robin Bradley Kar & Margaret Jane Radin, Pseudo-Contract and Shared Meaning
Analysis, 132 HARV. L. REV. 1135, 1140 (2019); see also Todd D. Rakoff, Contracts of Adhesion:
An Essay on Reconstruction, 96 HARV. L. REV. 1173, 1176, 1242, 1250–55, 1258 (1983) (suggesting that nonnegotiated, nonsalient boilerplate terms “ought to be considered presumptively . . . unenforceable”); LLEWELLYN, supra note 27 (arguing that consumers cannot R

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barriers are central themes in the new, proposed Restatement of Consumer Contracts, as well as in many cases and statutes.32 Part IV asks
what remains of this fulcrum if smart readers can offer a technological
solution to the reading problem. At the very least, the rise of smart
readers will change the terms of engagement between laissez-faire advocates and social reformers. It then considers the doctrinal adaptations necessary to adapt contract doctrine to smart readers.
The Article joins a few important contemporary conversations.
First, the debates on barriers to the reading and understanding of contracts are evergreen but recently became urgent given the imminent
vote on the new Restatement of Consumer Contracts. One central issue in the debate is the weight that should be given to consent to online terms and conditions.33 The possibility of smart readers shifts the
terms of the debate and may lead to a greater focus on market conditions and alternatives, than on term ignorance.
A second growing set of conversations concerns the relationship
between AI, discrimination, inequality, and access to justice. Scholars
hesion—Some Thoughts About Freedom of Contract, 43 COLUM. L. REV. 629, 632 (1943) (stating
the weaker party’s assent to standard contracts “is but a subjection more or less voluntary”);
Arthur Allen Leff, Contract as Thing, 19 AM. U. L. REV. 131, 143 (1970) (arguing that contracts
of adhesion are “not the product of a cooperative process, but the creation (essentially) of only
one of the parties”); Lewis A. Kornhauser, Comment, Unconscionability in Standard Forms, 64
CALIF. L. REV. 1151, 1162 (1976) (arguing that the majority of standardized terms “are candidates for non-enforcement”); Edith R. Warkentine, Beyond Unconscionability: The Case for Using “Knowing Assent” as the Basis for Analyzing Unbargained-for Terms in Standard Form
Contracts, 31 SEATTLE U. L. REV. 469, 472 (2008) (arguing that consumers’ assent to form contracts “is a fiction”); cf. RADIN, supra note 6 (criticizing the current legal regime and highlighting R
the need to tackle the harmful effects of harsh boilerplate terms).
32 See, e.g., DRAFT RESTATEMENT 2019, supra note 4. We return to this issue in more detail R
infra Section IV.A.
33 See Letter from Letitia James, N.Y. Att’y Gen., et al. to Members of Am. L. Inst. (May
14, 2019) (on file with the N.Y. Off. of the Att’y Gen.), https://ag.ny.gov/sites/default/files/letter_
to_ali_members.pdf [https://perma.cc/K7UE-4D2Y] (a letter from twenty-three attorneys general, critiquing the Draft reporter’s stance that a lax approach to mutual assent should be
adopted because consumers do not read or understand form contracts); Dee Pridgen, ALI’s
Restatement of the Law of Consumer Contracts: Perpetuating a Legal Fiction?, 32 LOY. CONSUMER L. REV. 540 (2020); Ian MacDougall, Soon You May Not Even Have to Click on a Website Contract to Be Bound by Its Terms, PROPUBLICA (May 20, 2019, 1:17 PM), https://
www.propublica.org/article/website-contract-bound-by-its-terms-may-not-even-have-to-click
[https://perma.cc/822W-PT6G]; Melvin Eisenberg, The Proposed Restatement of Consumer Contracts, if Adopted, Would Drive a Dagger Through Consumers’ Rights, YALE J. ON REG.: NOTICE
& COMMENT (Mar. 20, 2019) (censuring the Draft because its approach to terms is “farcical,” as
“[f]orm contract terms are normally obscure, legalistic, or both”), https://www.yalejreg.com/nc/
the-proposed-restatement-of-consumer-contracts-if-adopted-would-drive-a-dagger-through-consumers-rights-by-melvin-eisenberg/ [https://perma.cc/8EYG-P6HV]; see also infra notes 212–21 R
and accompanying text.

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are becoming growingly aware of the potential for bias and discrimination when algorithms make decisions.34 The Article meets these
conversations by showcasing that AI can contribute to positive change
when used to empower consumers. Smart readers can narrow gaps in
access to justice mostly through the channel of access to lawyeringlike services. They can also raise awareness of disparate treatment by
benchmarking individual offerings. At the same time, gaps in digital
inclusion and discrimination based on whether an individual uses a
smart reader can themselves exacerbate inequality.
Finally, there is a growing interest today among agencies, courts,
and digital platforms in adopting AI technologies to improve the regulatory and adjudicative process.35 Smart readers are a powerful addition to this arsenal because they allow easy benchmarking of industrywide practices and effective screening of abusive practices on a large
scale. Courts can employ smart readers to replicate the way individuals access the contract in question, thus improving the interpretative
process. The technology also allows for easy implementation of
“corpus linguistics”—a newly proposed technique of ascertaining
meaning by consulting actual modes of usage.36
Smart readers are here. As with any technology, they create winners and losers. Whether or how the potential of smart readers will be
realized depends not only on the technology but also on the legal environment that interacts with it. With further improvements rapidly
coming, it is thus high time to prepare for an age where machines can
read contracts.
34 The literature on that issue is quickly growing. For examples of important contributions,
see Sandra G. Mayson, Bias In, Bias Out, 128 YALE L.J. 2218 (2019); Deborah Hellman, Measuring Algorithmic Fairness, 106 VA. L. REV. 811 (2020); and Benjamin H. Barton & Deborah L.
Rhode, Access to Justice and Routine Legal Services: New Technologies Meet Bar Regulators, 70
HASTINGS L.J. 955 (2019).
35 See, e.g., DAVID FREEMAN ENGSTROM, DANIEL E. HO, CATHERINE M. SHARKEY &
MARIANO-FLORENTINO CUE´LLAR, GOVERNMENTBY ALGORITHM: ARTIFICIAL INTELLIGENCEIN
FEDERAL ADMINISTRATIVE AGENCIES (2020), https://www-cdn.law.stanford.edu/wp-content/
uploads/2020/02/ACUS-AI-Report.pdf [https://perma.cc/4KTS-PHWD]; Alicia SolowNiederman, Administering Artificial Intelligence, 93 S. CAL. L. REV. 633 (2020); Rory Van Loo,
Rise of the Digital Regulator, 66 DUKE L.J. 1267, 1324 (2017); Richard M. Re & Alicia SolowNiederman, Developing Artificially Intelligent Justice, 22 STAN. TECH. L. REV. 242 (2019) (exploring “robo-judging”).
36 Stephen C. Mouritsen, Contract Interpretation with Corpus Linguistics, 94 WASH. L.
REV. 1337 (2019); Omri Ben-Shahar, Data Driven Contract Interpretation: Discovering “Plain
Meaning” Through Quantitative Methods, JOTWELL (June 13, 2018), https://contracts.jotwell.com/data-driven-contract-interpretation-discovering-plain-meaning-through-quantitative-methods/ [https://perma.cc/32CF-ZXT7].

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I. SMART READERS: TECHNOLOGY AND CAPABILITIES
We start our discussion by mapping and illustrating the core capabilities of smart readers. Smart readers are built on machine learning
language models, and we particularly rely on a recent model known as
GPT-3. After offering a brief introduction of the technology, we consider its capabilities. In discussing these capabilities, we try to navigate
the problem that the technology is quickly evolving. Yet, specific examples are needed to ground the discussion. We find a middle ground
here by mapping core capabilities while using concrete examples from
an existing model. As we rely on current technology to produce the
examples, the reader will do well to consider our examples to be a
lower bound on future technological capabilities.37
Rather than focusing on technical detail, let us provide an intuitive sense of how the language models that power smart readers see
the world. At the core, a language model is a statistical representation
of human language. The model is the product of a machine-learning
process, which scours texts and learns to detect statistical patterns. An
important observation is that the language model does not learn to
read; it learns to see patterns. Although a native English speaker
would intuitively know to say, “great old green dragons” but not “old
green great dragons,” they would find it difficult to explain that logic
to a machine. A machine learning algorithm would simply learn that
the former phrase is 8.4 times more likely than the latter.38
One of the latest language models is called GPT-3.39 Produced by
the San Francisco-based nonprofit OpenAI,40 this language model was
trained on an immense collection of data, the smallest part being the
37 Within the time frame of writing this Article, Google has already produced an even
more ambitious language model, six times in parameter the size of GPT-3. Compare Tom B.
Brown et al., Language Models Are Few-Shot Learners, ARXIV (July 22, 2020), https://arxiv.org/
abs/2005.14165 [https://perma.cc/5ZDM-R8KT] (noting the GPT-3 model has 175 billion parameters), with William Fedus, Barret Zoph & Noam Shazeer, Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity, ARXIV (Jan. 11, 2021), https://
arxiv.org/abs/2101.03961v1 [https://perma.cc/SS64-XZNY] (noting the Google model has up to a
trillion parameters).
38 The example is based on MARK FORSYTH, THE ELEMENTSOF ELOQUENCE (2013).
39 See Brown et al., supra note 37. The technical approach is described in Alec Radford, R
Jeffrey Wu, Rewon Child, David Luan, Dario Amodei & Ilya Sutskever, Language Models are
Unsupervised Multitask Learners, OPENAI (Feb. 14, 2019) https://d4mucfpksywv.cloudfront.net/
better-language-models/language_models_are_unsupervised_multitask_learners.pdf [https://
perma.cc/V9X5-HLJ4]. For an accessible account, see, for example, Jay Alammar, How GPT3
Works—Visualizations and Animations, JAY ALAMMAR (July 27, 2020), http://jalammar.github.io/how-gpt3-works-visualizations-animations/ [https://perma.cc/9FEK-L9C6].
40 About, OPENAI, https://openai.com/about/ [https://perma.cc/GDG5-MBD7].

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entirety of Wikipedia.41 Based on its statistical analysis of these
sources, the model can produce convincing articles, poetry, horoscope
columns, a summary of movie plots in emojis,42 and even write some
not-too-bad comedy scripts.43 None of this is based on our notion of
understanding text; the model simply predicts which words should follow the user’s initial input. The model’s capabilities captured the imagination of both technologists and laypeople.44 Perhaps most
illustrative is the reaction of the philosopher of mind David Chalmers,
who called it “one of the most interesting and important AI systems
ever produced.”45
With this in mind, let us now examine the four key capabilities of
smart readers in the context of consumer contracts.
A. Simplification
It comes as little surprise to most that contracts feature complex,
long, and uninviting text. Judges, lawyers, policymakers, academics,
and laypeople share this intuition and routinely complain about it.46
There is broad agreement that contracts are hard to read because of
41 The model is trained on “45TB of compressed plaintext” which includes all of
Wikipedia and other (much larger) databases. Brown et al., supra note 37, at 8–9. R
42 For a collection of examples (including failed ones), see Gwern Branwen, GPT-3 Creative Fiction, GWERN (Sept. 28, 2020), https://www.gwern.net/GPT-3 [https://perma.cc/9YTA2RBA] (describing movie plots in emojis, including “Matrix: ‘ ’; The Hunger Games:
‘ ’”).
43 See Arram Sabeti, Why GPT-3 Is Good for Comedy, or: Don’t Ever Do an AMA on
Reddit, ARRAM (July 22, 2020), https://arr.am/2020/07/22/why-gpt-3-is-good-for-comedy-or-reddit-eats-larry-page-alive/ [https://perma.cc/W5N3-6BG8].
44 See, e.g., Karen Hao, These Weird, Unsettling Photos Show that AI is Getting Smarter,
MIT TECH. REV. (Sept. 25, 2020) (“Of all the AI models in the world, OpenAI’s GPT-3 has most
captured the public’s imagination.”); Amir HajiRassouliha, What Can GPT-3 Do to Accelerate
Conversational AI and Digital Human Innovation?, UNEEQ DIGIT. HUMS. (Sept. 1, 2020) (“The
incredible deep learning skills of GPT-3 have captured the imagination of the technology
community . . . .”).
45 David Chalmers, GPT-3 and General Intelligence, DAILY NOUS (July 30, 2020, 3:02 PM),
https://dailynous.com/2020/07/30/philosophers-gpt-3/ [https://perma.cc/H2EZ-4DT8].
46 See, e.g., Eisenberg, supra note 3; Korobkin, supra note 5, at 1233 (noting that form R
terms are often “hard to read, hard to understand, and hard to compare . . . .”); Jeffrey Davis,
Protecting Consumers from Overdisclosure and Gobbledygook: An Empirical Look at the Simplification of Consumer-Credit Contracts, 63 VA. L. REV. 841 (1977); Michael I. Meyerson, The
Efficient Consumer Form Contract: Law and Economics Meets the Real World, 24 GA. L. REV.
583 (1990); John Fry, Comic, CARTOONSTOCK, https://www.cartoonstock.com/cartoon?
searchID=CS116580 [https://perma.cc/GC6R-M7D6] (capturing this sentiment in a comic where
a manager speaks to his attorney, saying “These new Terms and Conditions you’ve drafted for us
are extremely long and overly complex—our customers are never going to be able to understand
them. Well done Jones!”).

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their semantic difficulty,47 length,48 formatting,49 and legalese.50 For example, one study of popular online consumer form contracts found
that these contracts were written at a level that matches academic
articles.51
The problem of complexity can be mitigated, if not defeated, by
the simplification of text. When done properly, simplification can alert
the reader to obligations that would otherwise be hidden in the prolix.52 Smart readers are increasingly adept at the task of reducing text
complexity. They do so by summarizing text, lowering language register, shortening text length, simplifying sentence structure, transforming formatting, and eliminating nonessential content.53 To illustrate,
consider a lessee confronting the following clause in the lease
agreement:
47 See, e.g., Uri Benoliel & Shmuel I. Becher, The Duty to Read the Unreadable, 60 B.C. L.
REV. 2255 (2019) (measuring the linguistic complexity of online consumer contracts); Michael L.
Rustad & Thomas H. Koenig, Wolves of the World Wide Web: Reforming Social Networks’ Contracting Practices, 49 WAKE FOREST L. REV. 1431, 1475 (2014) (studying the linguistic complexity
of social network contracts); Florencia Marotta-Wurgler & Robert Taylor, Set in Stone? Change
and Innovation in Consumer Standard-Form Contracts, 88 N.Y.U. L. REV. 240 (2013) (documenting the complexity of end-user license agreements).
48 See Aleecia M. McDonald & Lorrie Faith Cranor, The Cost of Reading Privacy Policies,
4 I/S: J.L. & POL’Y FOR INFO. SOC’Y 543, 563 (2008) (finding that it would take the average
consumer 244 hours—which equals 30.5 standard workdays—to read the privacy policies they
encounter online annually).
49 See Yonathan A. Arbel & Andrew Toler, ALL-CAPS, 17 J. EMPIRICAL LEGAL STUD.
862, 874 (2020) (finding in a large sample of consumer contracts that 77% included at least one
paragraph in all-caps).
50 See, e.g., Eisenberg, supra note 5, at 242; Hillman & Rachlinski, supra note 3, at 446, R
448, 479 (noting the difficulty posed by legal jargon).
51 See Benoliel & Becher, supra note 47. R
52 Reformatting can render the fine print less fine by displaying easier to read fonts, increasing spacing, and removing all-caps and other difficult formatting choices. On the failure of
capitalization to improve readability, see Arbel & Toler, supra note 49. R
53 See, e.g., Louis Martin, Angela Fan, E´ric de la Clergerie, Antoine Bordes & Benoıˆt
Sagot, Multilingual Unsupervised Sentence Simplification, ARXIV (May 1, 2020), https://arxiv.org/
pdf/2005.00352v1.pdf [https://perma.cc/89MS-47AD] (defining text simplification as “reducing
the complexity of the . . . text while retaining its original meaning”).

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8. EVICTION: If the TENANT does not pay the rent within
five (5) days of the date when it is due, the TENANT may be
evicted. The LANDLORD may also evict the TENANT if the
TENANT does not comply with all of the terms of this Lease,
or for any other causes allowed by law. If evicted, the TENANT
must continue to pay the rent for the rest of the term. The
TENANT must also pay all costs, including reasonable attorney
fees, related to the eviction and the collection of any moneys
owed to the LANDLORD, along with the cost of re-entering,
re-renting, cleaning and repairing the PREMISES. Rent received from any new tenant during the remaining term of this
lease will be applied by the LANDLORD to reduce rent only,
which may be owed by the TENANT.54
By scanning this text, the smart reader responds with the following output:55
The output features shorter sentences, and the language is easier to
parse than the original. It also employs the second person, avoids capitalized text, and uses contractions. Critically, it does all of that while
largely preserving the meaning of the original clause. Although a matter of judgment, we find the output much easier to parse.
54 Yonathan Arbel, Lease Agreement, BATTLE OF THE FORMS (Feb. 9, 2021, 3:16 PM),
https://baggleoftheforms.com/wp-content/uploads/2021/02/leade_residential.pdf [https://
perma.cc/SVC6-L4GV].
55 Screenshot of smart reader explanation [3] (on file with authors).

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Another more nuanced example is the following clause in an employment contract. As before, the original text included in the frame is
the input. It is then followed by the smart reader’s output.56
5. At Will Employment. The Board and [Employee] expressly
agrees that the employment relationship between the Board
and [the Employee] shall be considered at will. [Employee] may
terminate employment with the Board at any time, with or
without cause, and with or without prior notice. The Board may
terminate the employment of [Employee] at any time, with or
without cause, and with or without prior notice. This At Will
Employment Contract supercedes any contrary Board policies,
procedures, and/or statements of any kind or nature whatsoever. In the event of conflict between any Board policy or procedure and this paragraph, the provisions of this paragraph shall
govern.57
This explanation is remarkable for several reasons. First, despite
its simplicity, it captures the meaning quite well. Second, it is a bit
longer than the original, but it may nevertheless be more readable
because of its humorous, flippant tone.58 Third, it uses many illustra56 Screenshot of smart reader explanation [4] (on file with authors).
57 At Will Employment Contract, LAW INSIDER (Feb. 4, 2004), https://www.lawinsider.com/
contracts/242DHQfPUwj [https://perma.cc/W97X-L5ZL].
58 As suggested to us by Professor David Hoffman, humor degrades over time. This is a
challenge to firms who wish to adopt a lighter tone in their contracts but less so to smart readers.
We asked the smart reader for another output, and it produced the following:
You’re in the hospital, and you work at starbucks [sic]. . . . There’s a new policy at
Starbucks. All baristas must wear green lipstick. You don’t like green lipstick and
you feel like the policy is ridiculous, so you refuse to wear it. Your boss fires you for

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tive examples, which give the reader a vivid sense of the import of the
clause. Fourth, it contains significant legal errors, most worryingly
portraying racial discrimination as legal.59
Text summary is a process involving what data scientists call
“lossy compression.”60 That is, it runs the risk of losing some of the
original meaning of the text in the same way that summarizing Ulysses
as two men who spend a day walking around Dublin does.61 Yet, form
contracts are not literary masterpieces, and, in our context, such a
concern can be easily overstated. Given the high degree of contractual
bloat, considerable degree of text can be eliminated without risking
loss of meaning.62
In essence, smart readers do not merely summarize the text or
rephrase it. Rather, they take a liberal approach to the text and can
render dense paragraphs quite accessible, if not entertaining. In terms
of capability, then, smart readers can solve a host of issues related to
the linguistic complexity of contracts. As the following sections show,
despite being useful and impressive in many ways, simplification is
probably the least exciting part of smart readers.
B. Personalization
Individuals understand the world in different ways owing to their
inherent disposition, circumstances, upbringing, culture, idiom and
language, cognitive ability, and socioeconomic status.63 As all teachers
know, the same materials require fundamentally different presentanot following the dress code. . . . That’s when you remember the At Will Employment contract you signed when you started.
Screenshot of smart reader explanation [5] (on file with authors).
59 See Civil Rights Act of 1964, Pub. L. No. 88-32, 78 Stat. 241 (codified in scattered sections of 28, 42, 52 U.S.C.); Fair Housing Act of 1968, 42 U.S.C. §§3601–3619. This is an important risk that should be recognized throughout the Article, and we return to it infra Sections
III.E, IV.C.4.
60 See Lossy Compression, PCMAG, https://www.pcmag.com/encyclopedia/term/lossy-compression [https://perma.cc/U95W-F5WY].
61 See generally Anna Ga´t (@TheAnnaGat), TWITTER(July 4, 2020, 7:33 PM), https://twitter.com/TheAnnaGat/status/1279559029614874627 [https://perma.cc/D267-AK98] (prompting
other Twitter users to “describe your favorite NOVEL as boring as possible”).
62 The idea of contractual bloat is not without controversy. Precision is an important part
of the legal craft and precision may necessitate accounting for a large number of contingencies.
At the same time, the structure of incentives asymmetrically favors bloat: firms can discourage
reading and lawyers can add fees or save costs by reusing old forms. See generally Claire A. Hill,
Why Contracts Are Written in “Legalese,” 77 CHI.-KENT L. REV. 59 (2001) (discussing the history
of form contracts and their persistence).
63 See All in the Mind with Sana Qadar, WEIRD Psychology, ABC RADIO NAT’L (Oct.
18, 2020), https://www.abc.net.au/radionational/programs/allinthemind/weird-psychology/
12766212 [https://perma.cc/NS2W-SQ85].

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tions to different audiences. The same problem is relevant in consumer contracts, where a one-size-fits-all approach to disclosure
frequently fails. An obvious example is recent immigrants, some of
whom have less than a full command of the English language.
But the differences go much deeper. Research in psychology
shows, for example, that some individuals process information better
when it is presented in abstract terms, and others when it is grounded
in examples.64 Despite these challenges, the uniform approach to disclosure is thought to be unavoidable, given the cost of personalizing
contracts on the firm side.65
Although regulators understand the need to personalize contracts, they rarely require it of firms. The challenge personalization
poses to firms appears almost intractable because adapting contracts
to each specific consumer’s cognitive skills is arguably prohibitively
costly, difficult, and information intensive.66 In the rare occasions
where regulators set requirements on the presentation of contractual
text, such as in the case of the Schumer Box,67 the imaginary audience
is some more-or-less average reader, who has a more-or-less average
command of the English language, cognitive ability, and cultural
literacy.68
But what if the personalization can be made, not on the business
side, but on the consumer side? Like hearing aids that adjust the volume of speech to the specific needs of the listener, smart readers offer—for the first time—the ability to make such adaptations on the
consumer side. Smart readers can tailor textual presentation to be
highly sensitive to a specific user. For instance, it can consider her
cultural expectations, linguistic abilities, and cognitive needs.
64 See Beichen Liang & Joseph Cherian, Cross-Cultural Differences in the Effects of Abstract and Concrete Thinking on Imagery Generation and Ad Persuasion, 22 J. INT’L CONSUMER
MKTG. 187, 188 (2010).
65 With the advent of big data, there is a growing optimism about the power to personalize
contracts and contract rules. See, e.g.,Ariel Porat & Lior Jacob Strahilevitz, Personalizing Default Rules and Disclosure with Big Data, 112 MICH. L. REV. 1417 (2013). For a discussion of the
risks of personalization see infra Section III.E.
66 See infra Section III.E.
67 12 C.F.R. §226.5 (2021) (requiring firms to clearly disclose the costs of credit cards and
specifically prescribing such disclosure to ensure unity among different credit cards issuers).
68 See, e.g., Heinz W. Kirchner, 63 F.T.C. 1282, 1290 (1963) (“An advertiser cannot be
charged with liability in respect of every conceivable misconception . . . among the foolish or
feeble-minded.”); Am. Home Prods. Corp., 98 F.T.C. 136, 371 (1981), aff’d, 695 F.2d 681 (3d Cir.
1982) (relying on what “would reasonably have been understood by consumers”); Bristol-Myers
Co., 102 F.T.C. 21, 320 (1983), aff’d, 738 F.2d 554 (2d Cir. 1984) (“[A]ds must be judged by the
impression they make on reasonable members of the public.”).

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Consider the former example of an at-will clause. This time, however, the reader is Luis, a recent immigrant from a Spanish-speaking
country. His specific output will be:69
This output is not just a translation. Beyond presenting it in the
right language, from Luis’s perspective, the smart reader also simplifies the contract and makes it more accessible to his specific needs.70
Linguistic personalization easily accommodates the needs of those
who may be comfortable with English but prefer simpler words,
shorter sentences, more explanations, or concrete examples.
Another example comes from the choice of law clause presented
above. As the reader may recall, the original term reads:71
12. Controlling Law and Severability. This License will be governed by and construed in accordance with the laws of the State
of California, excluding its conflict of law principles. This License shall not be governed by the United Nations Convention
on Contracts for the International Sale of Goods, the application of which is expressly excluded. If you are a consumer based
in the United Kingdom, this License will be governed by the
laws of the jurisdiction of your residence. If for any reason a
court of competent jurisdiction finds any provision, or portion
thereof, to be unenforceable, the remainder of this License shall
continue in full force and effect.
69 Screenshot of smart reader explanation [6] (on file with authors).
70 There is a great deal of linguistic diversity among Spanish speakers, so this example only
captures one layer of heterogeneity. On the complexity of translating the legal term “at-will”
from a Chilean perspective, see Carlos Herna´ndez Contreras, Origen y Evolucio´n del Despido
(At-will Employment) en Los Estados Unidos de Norteame´rica, inESTABILIDADENEL EMPLEO
141, 142 (2017) http://www.derecho-trabajo.cl/wp-content/uploads/2019/02/EstabilidadEnElEmpleo2.pdf [https://perma.cc/6L6L-WGCX]. For further discussion of dialects, see infra text accompanying note 74. R
71 APPLE, supra note 1, ¶ 12.

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When the user asks the smart reader to adapt it to a young person, it produces the following output:
The smart reader offers a lucid explanation. It has short and direct sentences, clearly identified “characters,” and a logical narrative.
We believe this specific choice of language would be very accessible to
young adults, markedly more than the original. This does not resolve
the problem of understanding what California law entails, but at least
it offers an initial grasp of the clause and questions that need to be
asked.
Alternatively, consider the following example of a sixteen-yearold who tries to understand what luggage he can bring with him on his
trip. Scanning the airline ticket terms and conditions, he comes across
the following clause:72
C. Excess and Oversize/Overweight Baggage Limits and
Charges
1. Except as otherwise provided in the terms of this Contract of Carriage or by law, articles transported as Checked
Baggage may not exceed the Maximum Outside Linear Dimensions of 115 linear inches (292 cm) or a maximum weight of 99.9
pounds (45.3 kg).
2. UA may, in its sole discretion, change, consider or make
exceptions to its Excess or Oversize/Overweight Baggage policy
(e.g., to the number, size, weight, type and/or applicable service
charges).
3. Charges apply for Excess and Oversize/Overweight Baggage, in addition to applicable Baggage service charges(s) required to be paid pursuant to UA’s general Baggage Allowance
policy. These charges apply each way (i.e., based on a one-way
trip) and are cumulative (i.e., Baggage that is excess and also
oversized and/or overweight will be subject to both Excess Baggage and Oversize/Overweight Baggage charges).
This time, the smart reader responds with the following output:
72 Contract of Carriage Document, UNITED (Nov. 8, 2021), https://www.united.com/ual/en/
us/fly/contract-of-carriage.html#tcm:76-6640 [https://perma.cc/6YMY-SR85].

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Beyond its simplification and summary of the text, the output
uses a choice of words that would be easy for a teenager to process.
Rather than talking about excess fees and service charges, the output
simply notes that the airline will “charge you extra money.” Using
straightforward language that focuses on direct implications would be
easily recognizable by most teenagers. The reader also explains the
idea of cumulative charges in a highly intuitive way: it uses a concrete
example of a bag that is too large and too heavy. The examples
ground the abstract fee model in a concrete object, making it easier to
grasp. Such personalization is a powerful solution for people who have
not developed abstract conceptual reasoning or for those who dislike
it.73
Another capability of personalization is quite subtle. Consider
the dialectical differences within subgroups, such as “regionalisms.”
For simplicity, the following example uses a noncontractual clause
spoken by a hypothetical Alabamian and asks the model to adapt it to
a Bostonian:74
“I was fixin to tell y’all about how the other day I ran out of
maters and taters. I parked my car cattycorner from the store.
Inside I saw some boiled goobers and slaw that was fit as a
fiddle but because I didn’t have a bag to tote it all, I used a
buggy.”
This example is particularly interesting because private dictionaries are a sore problem in contract law. What is a judge to do when the
73 The reader might notice that the answer does not capture the content of clause C.2.
While there is always a cost to omitting text—lossy compression—we believe most human readers would likely focus on the other clauses as well.
74 Screenshot of smart reader explanation [7] (on file with authors).

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parties use Peerless to refer to two different ships?75 What is a
chicken: a young chicken or an older stewing chicken?76 Must a seller
of four-inch square studs deliver studs that are four inches by four
inches?77 If a New Yorker orders Coke from an Alabamian, must the
latter provide Coca Cola or would other types of carbonated drinks be
permitted?78 Personalization inches parties toward shared
dictionaries.
A last feature of personalization is that it can be intersectionally
rich—such as providing custom output to an elderly woman from rural
Louisiana. If a consumer is both young and Spanish speaking, it will
be trivial to first take the airline ticket and have it processed for a
young reader, and then take the resulting output and process it for a
Spanish-speaking person. The range of mix-and-match possibilities is
broad indeed.
In sum, personalization offers a textual adaptation that is bespoke to the needs of the specific reader. Critically, the adaptation is
made on the consumer side. It therefore saves firms the challenge of
acquiring information about every customer’s specific needs and
adapting their disclosures accordingly. Personalization can mitigate
the problem of private dictionaries between contracting parties, even
if it does not entirely solve it.
C. Construction
To understand a legal text, one needs more than mastery of the
language. The classic distinction between interpretation and construction reflects this idea. As used by Arthur Corbin,79 construction is the
act of extracting the legal significance of contractual terms—a process
that goes beyond the parsing of words. In the scholarship, the construction function is mostly employed in the context of judges approaching texts. Yet, it is worth recognizing that parties and their
lawyers also construct legal text, even if they do so less authoritatively.
75 See Raffles v. Wichelhaus (1864) 159 Eng. Rep. 375. One innovative solution is to give
both parties the option to enforce their understanding of the contract. See Benjamin Alarie,
Mutual Misunderstanding in Contract, 46 AM. BUS. L.J. 531, 533–34 (2009).
76 See Frigaliment Importing Co. v. B.N.S. Int’l Sales Corp., 190 F. Supp. 116 (S.D.N.Y.
1960).
77 See Abramov v. Home Depot, Inc., No. 17-cv-1860, 2018 WL 1252105, at *5 (N.D. Ill.
2018) (dismissing the plaintiff’s claims, even though the boards were only 3.5” by 3.5” inches).
78 See POPVS SODA, https://popvssoda.com [https://perma.cc/R8UD-H5UH].
79 For Arthur Corbin’s view of the distinction, see Gregory Klass, Interpretation and Construction in Contract Law 13 (Jan. 19, 2018), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2913228 [https://perma.cc/LD7W-7T4Y].

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Using a smart reader, the consumer can ask specific follow-up
questions about the meaning of the text. Suppose, for example, that a
person is about to buy a car. The contract with the dealership states
that the car is bought “as is.” The term itself is not semantically complex, but its legal meaning may well elude the buyer. Indeed, courts
frequently grapple with cases where car buyers had a fundamentally
different understanding of the term from that intended by the dealership.80 Does an “as is” clause preclude returning the car if it turns out
later that the engine has a major defect? Can the buyer demand reimbursement if the seller did not disclose material problems? Does “as
is” include all types of defects or problems, known and unknown at
the time of purchase?81
Inputting the “as is” clause to the smart reader results in the following output:82
The output explains the legal consequences of buying a car “as
is.”83 The smart reader maps the legal concept of “as is” to its legal
consequences straightforwardly, the way a lawyer might. Impressively,
the smart reader achieves this while also simplifying and personalizing
the contract by using the second person, contractions, and repetition.
Another example of construction is a credit agreement that contains, among its many clauses, this one: “You will be in default
if: . . . (7) you permanently reside outside the United States.”84 This
clause, while linguistically simple, can still raise challenges. Would a
trip abroad trigger this clause? Does the consumer risk their credit
80 See Scott J. Burnham, The Parol Evidence Rule: Don’t Be Afraid of the Dark, 55 MONT.
L. REV. 93, 126 (1994).
81 Curtis v. Bill Byrd Auto., Inc., 579 So. 2d 590 (Ala. 1990) (holding that despite an as-is
clause, the dealership had a duty to disclose negative information known to it).
82 Screenshot of smart reader explanation [8] (on file with authors).
83 We again meet the constraint that simplification is “lossy.” Car sales are also regulated
by state laws—in particular, lemon laws—and the enforceability of contractual clauses is subject
to a variety of defenses (most pertinently, fraud).
84 CAPITAL ONE, CUSTOMER AGREEMENT 4, https://www.capitalone.com/media/doc/
credit-cards/BR281646_M112863_CA358_LetterSize.pdf [https://perma.cc/PRY9-3D66].

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score if they stay overseas with their family for a few weeks? To resolve this, the consumer can simply ask the smart reader:85
“Laura, if I travel to England for one week to visit my
grandmother, would I be in default?”
This answer is accurate, though construction is a far more complex task than interpretation. Indeed, there is often little agreement
among lawyers regarding the meaning of a specific clause, and judges
frequently differ in their construction of legal texts. This means that
we cannot expect smart readers to offer authoritative construction in
the near future; but at the same time, even if limited, the smart
reader’s construction might not be far from the accuracy of a reasonable lawyer. It is also worth remembering what H.L.A. Hart once observed: penumbral cases of interpretation may be “the daily diet of
the law schools,” but they are not the majority of cases.86 Many mundane questions, such as “can I sublet my apartment?” do not require a
great deal of legal mastery if the contract explicitly prohibits subleases. This capability, however, also raises questions regarding the
unauthorized practice of law.87
D. Benchmarking
Arguably, the most powerful capability of smart readers is that of
benchmarking. Smart readers can assign a contract a score or stars
based on how it compares with other contracts in the market.
Comparison shopping is an information-intensive activity, as buyers often care about a great variety of factors. These may include
price, quality, design, environmental impact, delivery, the product’s
lifespan, seller’s reputation, and—importantly—the contract terms.
Consequently, effective comparison shopping involves considerable
search costs and cognitive effort.88 The consumer needs to amass the
85 Screenshot of smart reader explanation [9] (on file with authors).
86 H.L.A. HART, Positivism and the Separation of Law and Morals, in ESSAYS IN JURISPRUDENCEAND PHILOSOPHY 49, 72 (1983).
87 For a review of the surrounding issues, see generally Frank Pasquale, A Rule of Persons,
Not Machines: The Limits of Legal Automation, 87 GEO. WASH. L. REV. 1 (2019). See also Barton & Rhode, supra note 34 (calling for lax regulatory approach to online technologies that R
increase access to justice).
88 See, e.g., Rory Van Loo, Helping Buyers Beware: The Need for Supervision of Big Retail,

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information, process it, and draw comparisons. Even the mundane
purchase of toilet paper involves comparisons that exponentially grow
in complexity.89 It is little wonder that psychologists find a phenomenon of choice overload, which might lead to analysis paralysis.90
Benchmarking offers a cost-effective solution to this problem.
One early (in machine learning timescale) example was in 2014. Researchers from Columbia University built a machine learning model
that classifies and ranks privacy policies.91 The model proved highly
accurate in identifying whether the policy includes terms such as ad
tracking, encryption of information, or profiling.92 Based on this analysis, the model was able to state whether a contract included uncommon terms. For example, a profiling term was present in 52% of
agreements, whereas 74% of contracts limited the duration of retention of private information.93 Taking this approach a step further, the
researchers developed the means to score privacy agreements based
on the inclusion or exclusion of certain terms. This way, the model was
able to scan a privacy policy and assign it a grade of “A,” “B,” or “C,”
as the case may be.94
Such benchmarking capabilities have recently been deployed. In
2021 researchers from the University of Texas at Austin released
“PrivacyCheck,” a browser extension that provides a free and on-demand ranking of privacy policies.95 Built on machine learning algorithms,96 Figure 1 below illustrates the operation of this tool, which
163 U. PA. L. REV. 1311, 1327 (2015) (“The high cost of acquiring information on hundreds of
mass retail items among thousands of choices across different stores leads most consumers to
make decisions with limited comparative information.”).
89 Furthermore, for the consumer to be able to systematically aggregate all the relevant
information, each of the product’s attributes should be given a relative weight and mark, which
together will lead to a combined overall score. This is also known as the “weighted adding strategy.” See generally, e.g., James R. Bettman, Mary Frances Luce & John W. Payne, Constructive
Consumer Choice Processes, 25 J. CONSUMER RSCH. 187 (1998) (applying this strategy to consumer-related decisions).
90 See generally Justin Beneke, Are Consumers Really Bewildered by Overchoice? An Experimental Approach to the Tyranny of “Too Much,” 21 J. FOOD PRODS. MKTG. 90, 97 (2015);
BARRY SCHWARTZ, THE PARADOXOF CHOICE (2004).
91 Sebastian Zimmeck & Steven M. Bellovin, Privee: An Architecture for Automatically
Analyzing Web Privacy Policies, in PROCEEDINGSOFTHE 23RD USENIX SECURITY SYMPOSIUM
1 (2014).
92 Id. at 7–9.
93 Id. at 8–9.
94 Id.
95 PrivacyCheck: Overview, supra note 11.
96 RAZIEH NOKHBEH ZAEEM, SAFA ANYA, ALEX ISSA, JAKE NIMERGOOD, ISABELLE
ROGERS, VINAY SHAH, AYUSH SRIVASTAVA & K. SUZANNE BARBER, UNIV. TX. AUSTIN CTR.
FOR IDENTITY, PRIVACYCHECKV2: A TOOLTHAT RECAPS PRIVACY POLICIESFOR YOU 1 (2020),

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evaluates the policy’s compliance with twenty different privacy
questions.
FIGURE 1. PRIVACYCHECK INTERFACE
As Figure 1 illustrates, the model offers a clear overall score of
the privacy policy that the consumer reviews (65%) relative to the
mean score of privacy policies in that specific market (54%). It offers
a breakdown of some of the reasons for the score and, importantly,
provides website links to competitors who offer better privacy
policies.
The score provided by the smart reader is similar to the star ranking commonly shown next to products online. It would be possible to
augment product searches with this additional metric; it will not require much cognitive effort for consumers to incorporate such a methttps://identity.utexas.edu/sites/default/files/2020-10/PrivacyCheck%20v2%20A%20Tool
%20that%20Recaps%20Privacy%20Policies%20for%20You.pdf [https://perma.cc/T7W7BUEC].

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ric in their decision-making process. Star rankings do not always tell
the entire story, and many consumers seek written reviews.97 The
smart reader offers the possibility of an explanation as to the reason
beyond the scoring, which the consumer can weigh for themselves.
The smart reader can also direct the consumer to competitors
who offer better terms. In markets where sellers offer different terms,
such a tool can effectively mobilize consumers to the seller with the
best contract. Benchmarking accuracy may improve over time, as
smart readers become common. The greater database of contracts
could offer more fine-tuned assessments. Thus, benchmarking can
streamline many aspects of comparison shopping.
Of all four capabilities, benchmarking is at the most nascent
stage. The scoring of terms requires some tricky judgments, and at
times there will be ample room to contest any specific judgment. Indeed, ranking contracts is difficult for humans,98 and we are still far
from smart readers that can rival the ability of a seasoned lawyer to
rank contracts.99 Inaccurate benchmarking does not, however, produce meaningless information—despite the difficulty and inaccuracy
involved in ranking firms, consumers and regulators often rely on
reputational scores.100 As emphasized throughout this Article, what
we should have in mind in assessing the utility of smart readers is not
some abstract notion of accuracy, but rather the realistic alternative.
Even an imperfect benchmarking tool can offer considerable improvements if consumers do not read contracts and simply rely on their error-prone intuitive judgment.
II. SMART READER UPTAKE AND (NO) READING THEORIES
The capabilities of smart readers harbor great promise, yet this
potential matters little if consumers do not adopt the technology. The
principal purpose of this Part is to tackle the question of consumer
97 See generally Arbel, supra note 24 (discussing common failure mode in reviews and R
other forms of reputational information).
98 For more on this, see Shmuel I. Becher, A “Fair Contracts” Approval Mechanism: Reconciling Consumer Contracts and Conventional Contract Law, 42 U. MICH. J.L. REFORM 747,
765–67 (2009) (explaining why grading contracts is a challenging task).
99 See infra Section III.B (discussing errors and adversarial examples).
100 See FTC, THE “SHARING” ECONOMY: ISSUES FACING PLATFORMS, PARTICIPANTS &
REGULATORS 32 (2016), https://www.ftc.gov/system/files/documents/reports/sharing-economy-issues-facing-platforms-participants-regulators-federal-trade-commission-staff/p151200_ftc_staff_
report_on_the_sharing_economy.pdf [https://perma.cc/KC3N-68AW] (“[A] seller’s favorable
reputation can provide important leverage for regulators seeking to ensure consumers are protected when shopping online.”); Arbel, supra note 24, at 1262–85 (exploring reputational R
failures).

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uptake.101 On one level, uptake will be a function of the maturity of
the technology and its implementation. On a deeper level, uptake implicates a deeper question, namely, why is it that consumers do not
read contracts today? Surveying the literature, we map five classes of
theories that set out to explain the reading gap. These theories predict
different levels of uptake, some more optimistic than others, and we
shall argue below that even limited uptake can have broad market
implications.
Invoking the concept of Wittgenstein’s Ruler, this Part also aims
to learn something about the theories themselves. Famously, Wittgenstein argued that when one measures the table with a ruler, one is also
using the table to measure the ruler.102 By the same token, if, despite
theoretical predictions, uptake proves limited, this will provide a
meaningful lesson about our theories, their scope, and their validity.
Perhaps the ruler is broken.
On the technology side, uptake critically depends on its quality
and cost. It is also important to consider more prosaic factors of user
interface and user experience (“UI/UX”), which can make or break a
technology.103 Although these factors are currently unknown, there
are several reasons to believe that smart readers will be both effective
and affordable.104 First, current models of smart readers already produce impressive examples. Second, the rapid pace of improvements in
the field reinforces this optimism.105 Third, sophisticated firms already
stake billions of dollars on the outputs of their proprietary smart read101 See also infra Section IV.C (considering uptake by courts and agencies).
102 WITTGENSTEIN, supra note 26. R
103 See, e.g., Shruti Gupta, An Analysis of UI/UX Designing with Software Prototyping
Tools, in CROWDSOURCING AND PROBABILISTIC DECISION-MAKING IN SOFTWARE ENGINEERING: EMERGING RESEARCHAND OPPORTUNITIES 134 (Varun Gupta ed., 2020); Advent Tuban,
How UI/UX Design Can Make or Break Your Application, TECH. RIVERS (June 30, 2020), https:/
/technologyrivers.com/blog/how-ui-ux-make-or-break-your-application/ [https://perma.cc/VS5TDSYW].
104 In terms of user interface, the range of options is broad: a smart reader can be a browser
extension, a mobile app connected to the camera, a dedicated device, a service hosted on a
website, or an augmented reality extension.
105 See Danny Hernandez & Tom B. Brown, Measuring the Algorithmic Efficiency of Neural Networks, ARXIV (2020), https://arxiv.org/pdf/2005.04305.pdf [https://perma.cc/SYX8Q3GA]; see also Jared Kaplan et al., Scaling Laws for Neural Language Models, ARXIV (2020),
https://arxiv.org/pdf/2001.08361.pdf [https://perma.cc/23Q4-MCL2] (finding that language models “improve[] smoothly” as more compute becomes available, implying that large accuracy gains
are likely). An important commentator argues that based on model architecture alone, the next
iterations of GPT-3 are likely to yield considerable improvements. Branwen, supra note 42. R

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ers.106 These considerations suggest that a high degree of effectiveness
is possible.
The question of cost is somewhat harder because it requires us to
consider the monetization of the technology. On the cost side, the development of the model is certainly high, but the costs of “compute”—computational resources—are in exponential decline. Indeed,
variants of GPT-2 were made publicly available for free.107 If developers license smart readers to users, the cost depends on the developers’
business strategy. Most developers of mobile apps seem to pursue a
low-cost, broad-adoption strategy. Thus, low cost is feasible.108 Another business model is providing a free-to-use license and monetizing
the collection of consumer information. Firms may also pay developers in return for the promotion of their products, thus creating a natural conflict of interests. And we might also expect a mixed model,
where a basic app is freely available, but premium features are only
available to paying customers. Each of these strategies creates its own
problems. Still, it is fairly plausible to see how the app can become
affordable. Ultimately, if the last hurdle to the adoption of smart readers is cost, policymakers can always choose to employ the lever of
subsidies.
While we think effectiveness and affordability are plausibly solvable problems, a more troubling question is whether consumers want to
use smart readers. Answering this question requires some understanding of the theories that explain why consumers do not read their contracts in the first place. Below we briefly describe five prominent
theories and examine their implications for uptake.
(1) Readability theories posit that consumers do not read because
of the linguistic complexity of contracts.109 From this perspective,
smart readers offer a powerful and direct solution. Smart readers can
summarize, explain, and make the text accessible with a push of a button. The presentation can be made graphically pleasing, and the use of
106 See Cao et al., supra note 25 (documenting the extensive use of smart readers by firms R
as part of algorithmic trading).
107 OpenAI runs an open-source platform and has made variants of GPT-2, including the
largest version, available on its website. See Irene Solaiman, Jack Clark & Miles Brundage, GPT2: 1.5B Release, OPENAI (Nov. 5, 2019), https://openai.com/blog/gpt-2-1-5b-release/ [https://
perma.cc/L7ZG-X3FA].
108 For example, in September 2021, approximately 75% of mobile apps in the Android
mobile store cost $3 or less. L. Ceci, Paid App Price Distribution In the Google Play Store as of
September 2021, STATISTA (Oct. 1 2021), https://www.statista.com/statistics/271109/averageprice-android-apps/ [https://perma.cc/7YSL-4QDG].
109 See supra notes 46–51 and accompanying text. R

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humor may render the experience of reading contracts more entertaining. This theory predicts high levels of consumer uptake.
(2) Transactional expectations theory holds that reading is not
productive because the text is a weak predictor of the parties’ rights.110
It does not matter what the contract says as much as what the contract
“does.” Consumers come to transactions with developed expectations
of their contours—informed by experience, seller representations, media coverage, and reputational information. When a term in the fine
print violates these expectations, firms are reluctant to enforce it for
reputational considerations, and courts may refuse to enforce it as
well.111 Thus, the written word is not necessarily a good predictor of
the allocation of rights and duties under the contract.
Proponents of the transactional expectations theory would likely
predict limited uptake. However, consumers may still want to consult
a smart reader in transactions where they do not have settled expectations, where the potential for error is large, or where the stakes are
high. The adoption of smart readers will thus likely be domain
specific.
(3) Rational apathy theory holds that reading is not cost-effective.112 Although consumers bear the cost of reading with certainty,
actual contractual issues are only remote probabilities in the future. If
there is a limited ability to negotiate terms or few competing sellers,
reading is a losing proposition.113
Given the focus on cost, this theory predicts at least a certain degree of uptake. As smart readers reduce the cost of processing information, they should affect both the intensive and extensive margin—
that is, more consumers reading contracts, with each consumer read110 See Yonathan A. Arbel & Roy Shapira, Theory of the Nudnik: The Future of Consumer
Activism and What We Can Do to Stop It, 73 VAND. L. REV. 929, 955 (2020); Ayres & Schwartz,
supra note 4, at 550–51. R
111 See Arbel & Shapira, supra note 110, at 956; Shmuel I. Becher & Tal Z. Zarsky, Minding R
the Gap, 51 CONN. L. REV. 69, 78 (2019); Lucian A. Bebchuk & Richard A. Posner, One-Sided
Contracts in Competitive Consumer Markets, 104 MICH. L. REV 827, 830 (2006).
112 As Professor Epstein puts it: “[I]t seems clear that most consumers—of whom I am
proudly one—never bother to read these terms anyhow.” Richard A. Epstein, Contract, Not
Regulation: UCITA and High-Tech Consumers Meet Their Consumer Protection Critics, inCONSUMER PROTECTIONINTHE AGEOFTHE ‘INFORMATION ECONOMY’ 205, 227 (Jane K. Winn ed.,
2006); see also Eisenberg, supra note 5, at 241–43. Reportedly, Judge Posner did not read his R
home equity loan boilerplate. See Debra Cassens Weiss, Judge Posner Admits He Didn’t Read
Boilerplate for Home Equity Loan, ABA J. (June 23, 2010, 1:37 PM), https://
www.abajournal.com/news/article/judge_posner_admits_he_didnt_read_boilerplate_for_home_
equity_loan/ [https://perma.cc/LYC6-HAWU].
113 See, e.g., Hillman & Rachlinski, supra note 3, at 436 (“[C]ompetitors usually employ
comparable terms.”).

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ing more terms. At the very least, scoring should be an attractive feature because the cost of checking the score is comparable to the cost
of reviewing online reputational information. At least in markets with
competing sellers, a steep reduction in reading costs will lead to consumer uptake.
(4) Cognitive biases theories hold that various biases make consumers irrationally avoid reading contracts. Some possibilities that can
drive such an aversion include unrealistic optimism about the rarity of
product defects, feelings of being locked into the transaction by the
stage in which the contract is presented, or an erroneous projection of
one’s understanding of the transaction on all other parties.114
Smart readers would make little difference for the most intransigently optimistic or information-averse consumers. Nonetheless, for
their more levelheaded peers, as detailed below, the technology can
help overcome some of their cognitive frailties.115 According to these
theories, one therefore might expect uptake at least among those consumers who are “sophisticatedly unsophisticated”—that is, those who
recognize their limitations.
(5) Social norms theories suggest that reading is limited due to
situational and relational considerations. Examining documents too
closely may communicate distrust toward the counterparty, skepticism
about the transaction, or signal one’s intention to act noncooperatively.116 In the consumer context, one might feel social pressure
against reading if it delays others waiting in line117 or may paint one as
being difficult—a nudnik.118
114 See, e.g., Eisenberg, supra note 5, at 240–43 (grounding fine print ignorance in R
“bounded rationality, optimistic disposition, [and] systematic underestimation of risks”); Oren
Bar-Gill, Seduction by Plastic, 98 NW. U. L. REV. 1373 (2004) [hereinafter Bar-Gill, Seduction by
Plastic]; Shmuel I. Becher, Behavioral Science and Consumer Standard Form Contracts, 68 LA.
L. REV. 117, 167 (2007); Lawrence Solan, Terri Rosenblatt & Daniel Osherson, False Consensus
Bias in Contract Interpretation, 108 COLUM. L. REV. 1268 (2008) (arguing that individuals have
an inflated sense of how ordinary their interpretation really is); see also OREN BAR-GILL, SEDUCTIONBY CONTRACT (2012) [hereinafter BAR-GILL, SEDUCTIONBY CONTRACT].
115 See infra Section III.F.
116 See, e.g., Hillman & Rachlinski, supra note 3, at 448 (“Consumers will feel uncomfortable suddenly indicating distrust to the reassuring agent by studying terms covering unlikely
events.”); Debra Pogrund Stark & Jessica M. Choplin, A License to Deceive: Enforcing Contractual Myths Despite Consumer Psychological Realities, 5 N.Y.U. J.L. & BUS. 617, 671 (2009) (“[I]t
will often be uncomfortable for consumers to double-check [sellers] verbal statements. . . . [It] is
in essence like calling the person a liar.”).
117 See Becher, supra note 114, at 157 (“[O]ther customers display nervousness (or even R
impatience) toward those exceptional buyers who insist on reading . . . .”).
118 See Arbel & Shapira, supra note 110, at 931 (“[N]udniks are often derided as petty and R
vindictive.”); Amy J. Schmitz, Access to Consumer Remedies in the Squeaky Wheel System, 39

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On this view, uptake results are likely to be mixed. The greatest
uptake is expected when consumers are in private settings, such as
when they purchase a product online. Nevertheless, even in public settings, one might expect some uptake. The use of smart readers on
one’s phone is less salient and less obtrusive to the flow of the transaction than the careful perusing of a stack of documents. That said, the
use of smart readers may be limited when the buyer is in closer proximity to the seller or when the buyer is in social settings that discourage it.
* * * * *
Overall, there are competing theoretical predictions regarding
the rate and scope of adoption of smart readers. Taken together, these
theories sketch a realistic scenario with at least modest adoption. At
the same time, Wittgenstein’s Ruler remains in the background.119 We
will have learned something important even if uptake proves low. In
this scenario, readability theories will likely suffer the greatest blow,
given the weight they put on the difficulty of reading as an explanation for consumer behavior. But this scenario is also of import to rational apathy theories. If consumers care so little about, say, privacy
policies that they do not bother even checking the score on their app,
this should bear on the debate surrounding the privacy paradox and
privacy regulation.120 And if consumers do not use smart readers even
in the privacy of their homes, that will signal an important lesson for
those holding a social theory of the reading problem. For now, however, we shall leave these questions open and instead assume some
degree of adoption and focus on the broader market implications of
smart readers in this scenario.
III. SMART READERS: IMPLICATIONS
We emphasized throughout that it is difficult to predict uptake,
and that there are interesting lessons to be learned if consumers turn
their backs to this technology. Here we seek to understand the implications of the technology assuming a modest level of adoption. As we
PEPP. L. REV. 279, 296 (“As an initial matter, American culture generally frowns on complainers
and calls on consumers to maintain a ‘[s]tiff upper lip.’” (quoting Jerry Plymire, Complaints as
Opportunities, 5 J. SERVS. MKTG. 61, 61–62 (1991))).
119 WITTGENSTEIN, supra note 26. R
120 For a comprehensive review of the debate, see Daniel J. Solove, The Myth of the Privacy Paradox, 89 GEO. WASH. L. REV. 1 (2021). Professor Solove denies the paradox and argues
that there is little to be learned from consumer’s behavior in specific cases, given that the value
of privacy is more general.

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show, smart readers hold great promise and commensurate risk even
under this restrictive assumption.121
A. Matching, Search Costs, and Market Competition
Smart readers increase term transparency, making it easier for
consumers to read and understand their contracts.122 Greater term
transparency will have both micro and macro effects. On the microlevel, we would expect to see a much better fit of matching between
consumers and contract terms. As consumers become more aware of
the terms offered by different sellers, they also become better positioned to select the contracts that fit their preferences.123 Better
matching naturally increases the surplus from the transaction. Term
transparency also reduces search costs—the costs of acquiring information about products and their accompanying contracts—thus resulting in further welfare gain.124
More ambitious is the macro-level effect. By cutting down on
search costs, smart readers can jumpstart market-wide competition for
contract terms. Some argue that in perfectly competitive markets,
firms are expected to offer the most efficient terms,125 the same way
they will sell the most efficient product configuration.126 However, this
prediction can fail if consumers are unaware of terms because then
there will be little demand pressure to offer better ones. Indeed, when
consumers are ignorant of the terms of their contracts, competition
121 Our analysis considers each element in isolation, but as Professor Bradley observed, one
should account for the entire, interdependent ecosystem of consumer protection. See Christopher G. Bradley, The Consumer Protection Ecosystem: Law, Norms, and Technology, 97 DENV.
L. REV. 35 (2019).
122 See supra Part II.
123 Under the surface, there are distributional concerns if access to smart readers is not
equally shared by consumers. We return to this point infra Section IV.C.
124 See Eisenberg, supra note 5, at 243. R
125 See, e.g., George L. Priest, A Theory of the Consumer Product Warranty, 90 YALE L.J.
1297, 1307–08 (1981) (“[I]magine that all products are manufactured under conditions of perfect
competition, so that each characteristic of a product—including warranty terms—serves to optimize the welfare of some dominant class of consumers.”); Alan Schwartz & Louis L. Wilde,
Imperfect Information in Markets for Contract Terms: The Examples of Warranties and Security
Interests, 69 VA. L. REV. 1387, 1392 (1983) (“[W]hen a market is in competitive equilibrium,
firms provide goods and contract terms at the lowest possible cost consistent with the continued
existence of these firms.”).
126 See, e.g., Korobkin, supra note 5, at 1206 (“Terms that govern the contractual relation-R
ship between buyers and sellers are attributes of the product in question, just as are the product’s
price and its physical and functional characteristics.”); see also Douglas G. Baird, The Boilerplate
Puzzle, 104 MICH. L. REV. 933, 941 (2006); Arthur Allen Leff, Contract as a Thing, 19 AM. U. L.
REV. 131, 142–43 (1970).

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can make things worse: firms that offer inferior terms can charge
lower prices and corner the market.127
The informed minority theory salvages the efficiency of market
terms even in situations where the majority of consumers are unaware
of them.128 The key insight is that even a minority of informed consumers can exert sufficient demand pressure to force an efficient
equilibrium.
The informed minority was originally developed in the context of
consumers who read contracts. It was therefore seen like it suffered a
lethal blow by the accumulation of empirical data that shows that actual readership rates are very low.129 Smart readers may still breathe
new life into this theory, or at least a more modest version of it. If
consumers rarely read the contract in full today, they might be willing
to read a simplified version of it with a smart reader. It may even be
enough to form a substantial minority if consumers only scan the contract score assigned to it by the reader. Thus, even modest adoption of
smart readers can result in broad market changes.
To see how these dynamics resolve, take privacy policies. Today,
perhaps only few consumers read and understand them. Let us take as
given that firms react to consumer ignorance by offering one-sided
127 See Bar-Gill, Seduction by Plastic, supra note 114, at 1373 (“[C]ompetitive forces com-R
pel sellers to take advantage of consumers’ weaknesses.”); Korobkin, supra note 5, at 1206 R
(“Ironically, the consequence of market forces in a world of boundedly rational buyer decisionmaking is that contracts will often include terms that are socially inefficient, leave buyers as a
class worse off . . . .”). See generally Xavier Gabaix & David Laibson, Shrouded Attributes,
Consumer Myopia, and Information Suppression in Competitive Markets, 121 Q.J. ECON. 505
(2006) (arguing that information shrouding techniques can persist in competitive markets).
128 The firm is pressured to offer all consumers a standard form contract because it cannot
identify in advance which consumer belongs to the informed minority. When this is not the case,
an undesirable separating equilibrium can emerge. See discussion infra Section IV.C.
129 See Eyal Zamir, Contract Law and Theory: Three Views of the Cathedral, 81 U. CHI. L.
REV. 2077, 2102–03 (2014) (“[O]utside of the law-and-economics community, most people would
quite confidently say . . . that hardly a soul reads standard-form contracts.”); Yonathan A. Arbel
& Roy Shapira, Consumer Activism: From the Informed Minority to the Crusading Majority,69
DEPAUL L. REV. 233, 241 (2020) (“Exhibit A: Schwartz himself seems to believe that nobody
reads contracts these days.”); Bakos et al., supra note 4 (providing empirical evidence few users R
read end-user license agreements). Other studies find somewhat higher reading rates, and it is
arguable whether they can sustain informed minority equilibria. See, e.g., Shmuel I. Becher &
Esther Unger-Aviram, The Law of Standard Form Contracts: Misguided Intuitions and Suggestions for Reconstruction, 8 DEPAUL BUS. & COM. L.J. 199, 206 (2010) (presenting surveys indicating that most consumers are not likely to read typical consumer contracts ex ante); Robert A.
Hillman, Rolling Contracts, 71 FORDHAM L. REV. 743, 747 n.18 (2002) (noting that only 24% of
law students surveyed “indicated that they read the terms of rolling contracts”); Jeff Sovern, The
Content of Consumer Law Classes III, 22 J. CONSUMER & COM. L. 2, 4 (2018) (surveying consumer law professors of whom a majority (57%) “rarely or never” read consumer contracts).

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terms, and there is little variety of policies in the market. Once consumers start using smart readers and particularly their benchmarking
capabilities, firms might witness a change in market behavior. There
would be some migration of consumers toward firms with better privacy policies, even if these policies are still far from what consumers
desire. Importantly, smart readers do not need to be perfectly accurate for this to happen. Even if smart readers only give a general sense
of which privacy policies are better, they might be effective. Once
firms recognize increasing demand pressure, they will respond by offering increasingly better terms.
Such market dynamics depend on competition, but some markets
are dominated by a monopolist or an oligopoly. One response is that
consumer empowerment can also affect monopolists, as greater
awareness can lead consumers to reduce demand, lobby regulators for
change, and enlist watchdogs and consumer organizations. Another
response concerns market dynamics. Consider, for example, the market for search engines, and suppose a monopolistic firm that infringes
on consumer privacy dominates it. Assume also that consumers care
about their privacy but do not read privacy policies. A potential entrant to this market will worry that, even if it offers better privacy
policies, it will be difficult to attract users because few read the privacy policy. In contrast, in a world where consumers use smart readers, it will be significantly easier for an entrant with better policies to
establish themselves and attract users. Even limited adoption of imperfect readers in imperfect markets can drive positive changes.
So far, we linked term transparency to the informed minority argument and its ability to pressure sellers into offering better contract
terms. There are, however, three additional ways in which term transparency may pressure firms to revise their contracts. First, equipped
with smart readers, consumer watchdogs, consumer organizations,
nudniks, and the media may be better able to identify and highlight
egregiously imbalanced terms.130 This ex post threat may incentivize
firms to offer efficient contracts ex ante.131 Second, smart reader capabilities may allow regulators to pay more attention to the terms offered in their respective industries, thus allowing regulators to exert
tighter supervision. To ward off the regulator, firms sometimes offer
130 See Arbel & Shapira, supra note 129, at 252–53. R
131 See Paul R. Kleindorfer, What If You Know You Will Have to Explain Your Choices to
Others Afterwards? Legitimation in Decision Making, inTHE IRRATIONAL ECONOMIST72, 72–73
(Erwann Michel-Kerjan & Paul Slovic eds., 2010).

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improved terms.132 Finally, the spotlight effect suggests that the higher
salience attributed to contract terms may draw social censure or
greater moral introspection, thus producing pressure on the individuals who draft and authorize contracts to offer more favorable terms.133
B. Errors and Adversarial Attacks
The conceit of the discussion so far is that all the examples used
were cherry-picked. Such a selection is necessary to develop a sense of
tomorrow’s capabilities today. However, cherry-picking does run the
risk of exaggerating the power and accuracy of the technology. Smart
readers will make errors, and this Section considers three types of errors that can stymie their operation: isolated errors; correlated errors;
and errors due to manipulations, known as adversarial attacks.
Due to technological limitations, we expect smart readers to
make many errors in their interpretation of legal texts. At the same
time, humans also make mistakes, and lawyers are humans too.134 Machines have an important advantage in this regard: in contrast to fickle
humans, machine models are indefatigable and do not experience the
dread of ennui when faced with endless contracts. Evaluating the
prospect of mistakes should thus begin by evaluating the relative frequency of error between humans and machines.135
Fortunately, computer scientists devote considerable attention to
creating benchmarks that compare human and computer performance
on various tasks. Based on these benchmarks, AI models appear
nearly on par with humans on a variety of specific tasks, and the gap is
132 See, e.g., James Fallows Tierney, Contract Design in the Shadow of Regulation, 98 NEB.
L. REV. 874, 874–75 (2020) (arguing that firms may adopt high-quality terms in order to avoid
regulation).
133 Thomas Gilovich, Victoria Husted Medvec & Kenneth Savitsky, The Spotlight Effect in
Social Judgment: An Egocentric Bias in Estimates of the Salience of One’s Own Actions and
Appearance, 78 J. PERSONALITY & SOC. PSYCH. 211, 214 (2000); George Loewenstein, Cass R.
Sunstein & Russell Golman, Disclosure: Psychology Changes Everything, 6 ANN. REV. ECON.
391, 404 (2014).
134 See Lana Birbrair, To Be Happy Lawyers (and Human Beings), Eight Rules for Law
Students To Live By, HARV. L. TODAY (May 6, 2015), https://today.law.harvard.edu/to-be-happy-lawyers-and-human-beings-eight-rules-for-law-students-to-live-by/ [https://perma.cc/3V454WRB] (suggesting that law students can become both “happy lawyers and human beings” (emphasis added) (quoting statement by Professor Bruce Bromley)).
135 One common critique of AI models is that they are inscrutable, so it is hard to understand the reasoning behind decisions. However, as Professors Casey and Niblett remind us, the
operations of the human brain also elude us, and some proffered reasons for action are nothing
more than post hoc rationalizations. Anthony J. Casey & Anthony Niblett, A Framework for the
New Personalization of Law, 86 U. CHI. L. REV. 333, 355–56 (2019).

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closing rapidly.136 One such benchmark is the PIQA test, where one is
presented with a goal and has to choose among two strategies to accomplish it.137 For example, if the goal is to separate the egg white
from the yolk using a water bottle, the question will be whether
squeezing or pushing the bottle is more likely to achieve the goal. The
PIQA test is easy for humans, who score roughly 95% on it.138 Nevertheless, the challenge it poses for machines appears insurmountable,
as it requires goal-oriented reasoning and an intricate understanding
of physical reality. Is scooping egg white best done by vacuum or an
exertion of force? Surprisingly, language models perform extraordinarily well on the PIQA test and achieve 82.8% accuracy.139 At the time
of writing this, a new model was said to perform with 90% accuracy on
this test, although the evidence is not complete.140 Other examples
that compare human and state-of-the-art (“SOTA”) AI abilities include abductive reasoning (human: 93%, SOTA: 90%),141 common
sense inference (96% v. 94%),142 open book responses based on a set
of facts (92% v. 87%),143 and analysis of social motivations (88% v.
83%).144 By the time this Article goes to print, many of these comparisons will look dated: SOTA will improve, but the human benchmark
will not.
Still, we should be careful not to overstate the accuracy of smart
readers. Reading, evaluating, and benchmarking contracts are openended tasks that require domain-specific knowledge. None of the
136 The game of Go was long thought to be impervious to machines, until world champion
Lee Sedol was defeated by Google DeepMind’s AI program, AlphaGo, in 2016. Christopher
Moyer, How Google’s AlphaGo Beat a Go World Champion, ATLANTIC(Mar. 28, 2016), https://
www.theatlantic.com/technology/archive/2016/03/the-invisible-opponent/475611/ [https://
perma.cc/DWW8-F3X9].
137 See generally Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao & Yejin
Choi, PIQA: Reasoning about Physical Commonsense in Natural Language, ARXIV (Nov. 26,
2019), https://arxiv.org/pdf/1911.11641.pdf [https://perma.cc/E8X7-GU6H].
138 PIQA Leaderboard, YONATAN BISK: PIQA, https://yonatanbisk.com/piqa/ [https://per
ma.cc/BJ9Q-WMKE].
139 Id.
140 Submission Details: UNICORN Model, ALLEN INST. FOR AI: LEADERBOARD, https://
leaderboard.allenai.org/physicaliqa/submission/bsd309pbvhc9b55n46fg [https://perma.cc/6UVNEJVS].
141 aNLI Leaderboard, ALLEN INST. FOR AI: LEADERBOARD, https://leaderboard.allenai.
org/anli/submissions/public [https://perma.cc/WBL7-MRNE].
142 HellaSwag Leaderboard, ALLEN INST. FOR AI: LEADERBOARD, https://leader
board.allenai.org/hellaswag/submissions/public [https://perma.cc/DCT6-BVV4].
143 OpenBookQA, ALLEN INST. FOR AI: LEADERBOARD, https://leaderboard.allenai.org/
open_book_qa/submissions/public [https://perma.cc/779D-XFRY].
144 Social IQA, ALLEN INST. FOR AI: LEADERBOARD, https://leaderboard.allenai.org/
socialiqa/submissions/public [https://perma.cc/P634-2RKA].

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benchmarks today evaluate language models on performance in this
domain. Based on our experience using the models, we expect a large
degree of error in the near future and a nontrivial improvement within
a relatively short time.
Critically, smart readers are useful even if they are not as accurate as their human counterparts, so long as they have a cost, consistency, and accessibility advantage over the alternatives. We already
noted how, despite the potential for occasional error, firms stake billions of dollars on investment analyses produced by smart readers.145
Moreover, if errors are random—the smart reader sometimes interprets a term as pro-consumer, other times as pro-seller—we might still
expect smart readers to exert a macro-level effect on term competition. This is because random errors tend to cancel each other out on
average, and, in large markets, firms would respond to average effects.
A more pernicious problem is correlated errors, where smart
readers systematically misread or misinterpret certain contracts or
contract terms in specific ways. For instance, smart readers may systematically ignore arbitration clauses or interpret waiver clauses as
imposing liability on sellers. In light of the black-box nature of language models, it is hard to anticipate the areas in which correlated
mistakes will emerge. But given that such models rely on detecting
statistical patterns, correlated errors become a distinct possibility.
An important mitigating factor in this context is the gradual
adoption of smart readers. If smart readers prove highly unreliable,
individuals will not use them. Instead, they will rely on the (errorprone) default of either not reading, skimming the document, consulting a lawyer in exceptionally important transactions, or using external
cues and heuristics.146 The scope of harm resulting from errors, although still real, is bounded by consumers’ gradual adoption of the
technology. It is probable that consumers will first use smart readers
in situations where the stakes are sufficiently high to make it worthwhile to use a smart reader, but not too high to make mistakes too
145 See Cao et al., supra note 25 (documenting the extensive use of smart readers by firms R
as part of algorithmic trading).
146 Of course, using heuristics, cues, and intuitions to evaluate the quality of contract terms
is often likely to result in erroneous decision making and inefficiencies. See, e.g., BAR-GILL,
SEDUCTION BY CONTRACT, supra note 114 (explaining how consumers’ optimism and myopia R
may facilitate inefficient terms in various consumer markets); Jeff Sovern, Elayne E. Greenberg,
Paul F. Kirgis & Yuxiang Liu, “Whimsy Little Contracts” with Unexpected Consequences: An
Empirical Analysis of Consumer Understanding of Arbitration Agreements, 75 MD. L. REV. 1, 2
(2015) (reporting survey results that “suggest a profound lack of understanding about the existence and effect of arbitration agreements among consumers”).

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costly. It will thus take some time before consumers use smart readers
as a substitute for lawyers, but a much shorter time before they start
using them as a substitute for not reading at all—especially if platforms like Amazon and Yelp start integrating a contract score into
their listings.
Even more pernicious than correlated errors are adversarial attacks by interested firms.147 In essence, an adversarial attack is a
method of exploiting the statistical nature of machine learning models. It is defined as the use of “malicious inputs modified to yield erroneous model outputs.”148 Put simply, they are “optical illusions for
machines.”149 The essential idea is that by presenting an ever-soslightly modified version of the contract—what is known as an adversarial example—one can mislead smart readers into parsing the agreement in ways that are desirable to the attacker. Critically, as noted
recently by Google and Open AI machine intelligence researchers,
adversarial examples can “often transfer from one model to another,
allowing attackers to mount black-box attacks without knowledge of
the target model’s parameters.”150
To illustrate the problem, Figure 2 demonstrates how a firm can
manipulate the interpretation of an image—or text—by including subtle noise. The reader is welcome to attempt to identify any difference
between the two images.151
147 See Selbst, supra note 28. R
148 Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik &
Ananthram Swami, Practical Black-Box Attacks Against Machine Learning, ARXIV (Mar. 19,
2017), https://arxiv.org/pdf/1602.02697.pdf [https://perma.cc/J57Y-49PH].
149 Ian Goodfellow, Nicolas Papernot, Sandy Huang, Rocky Duan, Pieter Abbeel & Jack
Clark, Attacking Machine Learning with Adversarial Examples, OPENAI (Feb. 24, 2017), https://
openai.com/blog/adversarial-example-research/ [https://perma.cc/PR3K-ALV2].
150 Alexey Kurakin, Ian J. Goodfellow & Samy Bengio, Adversarial Machine Learning at
Scale, ARXIV (Feb. 11, 2017), https://arxiv.org/pdf/1611.01236.pdf [https://perma.cc/XNN9VCKP]; see also Mazaher Kianpour & Shao-Fang Wen, Timing Attacks on Machine Learning:
State of the Art, in I ADVANCESIN INTELLIGENT SYSTEMSAND COMPUTING 111, 123 (Yaxin Bi et
al. eds., 2020) (noting the difficulty of dealing with the “near infinite number of possible attacks”
and the relative quickness of developing new attacks).
151 Goodfellow et al., supra note 149. R

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FIGURE 2. ADVERSARIAL EXAMPLE OF A PANDA BEAR
The image on the left is the original image of a panda bear that
the model quickly and correctly identifies as a panda. The unscrupulous firm, however, can add what seems like random noise to the original image, producing the one on the right. To the human eye, both
images are identical. To the model the difference is stark: the image
on the right is not a panda but a gibbon monkey.152 Alternatively, consider the adversarial attack in Figure 3.
FIGURE 3. ADVERSARIAL EXAMPLE OF A STOP SIGN
To the naked eye, the image appears to be of a stop sign marred
by stickers—a common occurrence in the modern urban landscape. To
the digital eye, however, the difference is vast. The algorithm will confidently identify the sign as a right turn sign.153
152 Id.
153 See Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei
Xiao, Atul Prakash, Tadayoshi Kohno & Dawn Song, Robust Physical-World Attacks on Deep
Learning Visual Classification, ARXIV (Apr. 10, 2018), https://arxiv.org/pdf/1707.08945.pdf
[https://perma.cc/2LV4-ZX5E].

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What would adversarial attacks look like in the context of written
contracts? It is hard to give a clear example precisely because these
attacks trick the human reader. To give some sense of the problem, we
construct an example—but we caution that real-life attacks will be far
more subtle and would make our example look like an innocent party
trick. With that said, consider the two versions of the short contractual
clause appearing in the table below.
A TEXTUAL ADVERSARIAL ATTACK
Version 1 Version 2
The sellers waive all liability re-The sellers waive all liability resulting from defects in the prod-sulting from defects in the product. uct.
Both versions seem to say the same thing: the sellers wave liability. But to the smart reader, they are nothing alike. In the first column,
the sellers waive liability; in the second, they assume liability. What
drives this difference in interpretation? Before we divulge our
method, we invite the reader to contemplate a judge trying to understand whether this is an innocent smart reader error or a deliberate
manipulation. Here, we used a rudimentary attack: We added tiny 1pt
text in light gray between the words “sellers” and “waive” that reads
“don’t.” A consumer relying on the smart reader may be misled to
believe that the seller offers warranties, but when the time comes to
hold the seller accountable, the seller will point to the viewable text,
which clearly exempts the seller from liability.154 Note, the seller is not
relying on the hidden text, but on the text that is also visible to the
court. It is the consumer who would need to explain the source of the
error. Whatever one thinks of the difficulty of identifying our manipulation, real-life adversarial attacks will be orders of magnitude harder
to detect.
It is natural to misperceive unfamiliar risks and view them as remote. This is particularly problematic with adversarial attacks, given
both their unintuitive statistical nature and the fact that they have not
received much treatment in legal scholarship so far. But in a sense,
adversarial attacks are familiar. As the history of conspicuous disclosure illustrates, they have low-tech counterparts. Once lawmakers
adopted the rule that certain terms will only be enforced if they ap154 See infra Section IV.C.3. Detection of such attacks can be extremely difficult, and the
seller may be able to regard the AI’s smart reader’s mistake as an innocent error.

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pear conspicuously, firms quickly discovered how to produce a document that would appear conspicuous to the court but will not, in fact,
increase consumer awareness.155 The solution is to capitalize excessively long paragraphs of text, thus allowing firms to meet the rule
without incurring its cost. Although a clear-eyed analysis would suggest that capitalization of this nature is unlikely to improve understanding,156 this attack proved efficacious with the courts, who
routinely enforce such clauses.157
Adversarial attacks are risky precisely because they are unfamiliar and hard to detect.158 It will be exceedingly difficult to detect such
attacks in practice because a sophisticated seller can use multiple
methods that appear innocuous, such as altering font size, font color
and shade, register, document margins, spacing, and the order of
words in the sentence. In the event of litigation, sellers can easily
frame smart reader error as a problem with the technology and defend
contractual design choices as a legitimate exercise of drafting discretion. The courts’ slow response to ALL-CAPS urges caution concerning this new technological risk.159
C. Access to Justice
Access to the legal system is unequally distributed. One hurdle in
this context is the cost of attorneys and the legal process.160 Another is
that the stakes of litigation are different for one-time players and repeat players, granting the latter an advantage.161 There are a variety of
other social barriers to justice,162 including the so-called “legal
155 See Arbel & Toler, supra note 49, at 866–72 (reviewing the history of legislation requir-R
ing the use of capital letters and conspicuous disclosure).
156 See OFFICEOF INV. EDUC. & ASSISTANCE, SEC, A PLAIN ENGLISH HANDBOOK: HOW
TO CREATE CLEAR SEC DISCLOSURE DOCUMENTS 72 (1998) (suggesting that text should not be
written in all-caps); see also Arbel & Toler, supra note 49, at 875–83 (providing experimental R
evidence suggesting the failure of all-caps); In re Bassett, 285 F.3d 882, 886 (9th Cir. 2002)
(“[T]here is nothing magical about capitals. . . . Lawyers who think their caps lock keys are
instant ‘make conspicuous’ buttons are deluded.”).
157 See Arbel & Toler, supra note 49, at 877–78, 878 n.88. R
158 See infra Section IV.C.3.
159 See Arbel & Toler, supra note 49, at 871. R
160 See BURDGE, supra note 22, at 26 (“[T]he average hourly rate for the typical Consumer R
Law attorney in the United States is $345 . . . .”); Edward L. Rubin, Trial by Battle. Trial by
Argument., 56 ARK. L. REV. 261, 288 (2003).
161 See generally Marc Galanter, Why the “Haves” Come Out Ahead: Speculations on the
Limits of Legal Change, 9 LAW & SOC’Y REV. 95 (1974).
162 See, e.g., Myriam Gilles, Class Warfare: The Disappearance of Low-Income Litigants
from the Civil Docket, 65 EMORY L.J. 1531 (2016) (explaining that limiting class actions may
have a particular adverse effect on marginalized and low-income consumers).

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deserts” that affect rural America.163 This raises deep concerns about
the ability of vulnerable consumers to learn about their rights and protect them.164
One path to increase consumers’ access to justice is to subsidize
the legal process through free lawyering services and reduced fees and
costs.165 Under this proposal, free or reduced-cost lawyers could assist
consumers in learning about their legal rights and enforcing them.
This solution, however, faces a critical flaw: it fails to scale. The cost of
providing subsidized access to a meaningful share of the consumer
body appears prohibitive.166
Smart readers can relieve some of this pressure by providing ondemand know-your-rights services.167 Consider again the example of
Ms. Williams, who entered a cross-collateral rent-to-own agreement.168 Given that the court itself called the relevant clause “obscure,”169 it is safe to assume that many consumers would lack a
critical understanding of the operation of cross-collateral agreements.
Now suppose that Ms. Williams had a smart reader installed on her
phone—after all, most people in poverty own smartphones today.170
163 See, e.g., Lisa R. Pruitt, Amanda L. Kool, Lauren Sudeall, Michele Statz, Danielle M.
Conway & Hannah Haksgaard, Legal Deserts: A Multi-State Perspective on Rural Access to Justice,13 HARV. L. & POL’Y REV. 15 (2018) (analyzing the problem of rural access to justice); see
also Ann M. Eisenberg, Distributive Justice and Rural America, 61 B.C. L. REV. 189, 193 (2020)
(proposing a general narrative according to which the “rural story” raises questions of fair allocation of benefits and burdens).
164 See generally Schmitz, supra note 118, at 290 (detailing business practices and consum-R
ers contracting behaviors that “work[] to advantage the most powerful and desirable consumers,
thereby fostering contractual discrimination and widening the gap between the consumer ‘haves’
and ‘have-nots’”).
165 For presentation and critique of participation-based solutions to the access to justice
problem, see Yonathan A. Arbel, Adminization: Gatekeeping Consumer Contracts, 71 VAND. L.
REV. 121, 157–71 (2018).
166 See id. at 159–60.
167 This capability can either complement or substitute away from various proposals to protect consumers through consumer education. See, e.g.,Schmitz, supra note 118, at 319; Meirav R
Furth-Matzkin & Roseanna Sommers, Consumer Psychology and the Problem of Fine-Print
Fraud, 72 STAN. L. REV. 503, 543 (2020).
168 See supra notes 12–16 and accompanying text. R
169 See supra note 15 and accompanying text. R
170 As of 2021, smartphones are in use by 76% of low-income adults. Emily A. Vogels,
Digital Divide Persists Even As Americans with Lower Incomes Make Gains in Tech Adoption,
PEW RSCH. CTR., (June 22, 2021), https://www.pewresearch.org/fact-tank/2019/05/07/digital-divide-persists-even-as-lower-income-americans-make-gains-in-tech-adoption/ [https://perma.cc/
B6ZU-UMF3]. However, there is still a persistent digital divide among consumer groups. See
infra Section IV.C.2.

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The app would have alerted her to the risk of the contract, explained
the relevant terms, and perhaps directed her to competitors.171
The expected low cost and convenience of smart readers would
enable many consumers to get better information about their legal
rights without the need for an attorney. Of course, smart readers are
not likely to be as good as lawyers, at least not in the short to medium
term. At the same time, there is some evidence that lawyers’ advice
can be racially biased.172 More generally, if the realistic alternative to
smart readers is not a lawyer but one’s own faculties, smart readers
can be a source of succor for many individuals who currently find justice inaccessible.
D. Compliance and Overcompliance
Smart readers can greatly increase compliance on both the seller
and the consumer side. A party, however well-meaning, will not be
able to comply with terms of which they are unaware. To the extent
that smart readers can facilitate awareness, comprehension, and recall
of contractual terms, they could increase overall compliance. This, in
turn, may minimize the risk of an inadvertent breach of contract. It
may also reduce the chances of contractual conflicts and disputes
more generally.
This benefit may be significant in cases when a party is trying to
refresh herself quickly on some aspects of the contract—say, whether
she can sublease their apartment or cancel a subscription before the
end of the trial period. This feature is also useful in cases where the
consumer wants to remind the seller of the latter’s obligations. With a
clearer understanding of their contractual rights, consumers can, for
instance, more easily demand that the seller repair a defective laptop
or offer compensation in the case of late delivery.
There is a risk, however, that smart readers may also inadvertently lead to overcompliance. Empirical studies show that many form
contracts include illegal and unenforceable terms.173 Despite their
unenforceability, such clauses seem to exert a considerable effect on
171 See supra note 16 and accompanying text. R
172 See Jean Braucher, Dov Cohen & Robert M. Lawless, Race, Attorney Influence, and
Bankruptcy Chapter Choice, 9 J. EMPIRICAL LEGAL STUD. 393, 412 (2012) (finding, in a vignette
study, that lawyers tend to advise Chapter 13 bankruptcy more often when the hypothetical
debtors have typical African American names).
173 See, e.g., Meirav Furth-Matzkin, On the Unexpected Use of Unenforceable Contract
Terms: Evidence from the Residential Rental Market, 9 J. LEGAL ANALYSIS 1 (2017); Meirav
Furth-Matzkin, The Harmful Effects of Unenforceable Contract Terms: Experimental Evidence,
70 ALA. L. REV. 1031, 1038–39 (2019) [hereinafter Furth-Matzkin, The Harmful Effects].

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individuals who erroneously suppose they are binding: a recent study
found that noncompete clauses are equally effective in states that enforce them and states that do not.174 Based on various studies, it seems
that laypeople generally take contracts too seriously:175 they erroneously assume that contract terms are strictly enforced176 and do not
consider that certain terms may be unenforceable.177 Some scholars
also argue that consumers attach moral significance to the written
word of the contract, believing that there is a duty to comply with
terms even if they are otherwise unenforceable.178 The emerging picture is therefore one where consumers often overestimate the validity
and import of contractual terms.
One unappreciated implication of this body of research is that
some degree of reading may be harmful. If consumers give terms that
are “clearly vulnerable to challenge . . . an unwarranted level of deference,” then perhaps reading does more harm than good.179 This research suggests that disclosure can be risky, and, on this view, smart
readers may further induce excessive levels of compliance that can
harm consumers.180
E. Discrimination and Personalization
Traditionally, scholars “viewed standard form contracts unfavorably and personalized contracts favorably.”181 Algorithmic personaliza174 See Evan Starr, J.J. Prescott & Norman Bishara, The Behavioral Effects of (Unenforceable) Contracts, 36 J.L. ECON. & ORG. 633, 655 (2020).
175 See generally Tess Wilkinson-Ryan & David A. Hoffman, The Common Sense of Contract Formation, 67 STAN. L. REV. 1269 (2015) (exploring lay understanding of contract
formation).
176 See, e.g., Tess Wilkinson-Ryan, The Perverse Consequences of Disclosing Standard
Terms, 103 CORNELL L. REV. 117, 164–65 (2017) (finding that the mere stipulation of policies
and rules in standard terms leads laypeople to view them as more legitimate and enforceable).
177 See, e.g., Dennis P. Stolle & Andrew J. Slain, Standard Form Contracts and Contract
Schemas: A Preliminary Investigation of the Effects of Exculpatory Clauses on Consumers’ Propensity to Sue, 15 BEHAV. SCIS. & L. 83 (1997) (highlighting the chilling effect that exculpatory
terms may have on consumers); see also Furth-Matzkin & Sommers, supra note 167, at 541 (dis-R
cussing the business practice of making verbal promises that are negated in the fine print).
178 See, e.g., Furth-Matzkin, The Harmful Effects, supra note 173, at 1058–59; Wilkinson-R
Ryan, supra note 15, at 1748; Wilkinson-Ryan, supra note 176, at 121–22. R
179 Wilkinson-Ryan, supra note 176, at 172. Wilkinson-Ryan considers a situation where the R
consumer agrees ex ante and only consults the terms ex post.
180 Id. at 121 (warning that accessible terms might be counterproductive to consumers because readable terms can be seen as more legitimate, even if they are one-sided and unfair). A
related concern is that courts will assume greater opportunity to read the text. See infra Section
IV.C.2.
181 Arbel & Shapira, supra note 110, at 985. R

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tion of contracts, however, flipped the locus of suspicion.182 Today,
scholars are increasingly aware that with big data, sellers can offer
personalized contracts that target vulnerable consumers with a high
degree of precision.183 In the past, redlining was done crudely based
on zip codes as a proxy for race.184 Personalized contracts allow sellers
to redline with a pencil rather than a sharpie.
Smart readers offer some redress. When a consumer is offered
unusual terms, perhaps because of their race or ethnicity, the smart
reader can benchmark that for them. For instance, if a lender offers a
marginalized consumer a high-interest rate, the smart reader can alert
the consumer via an output such as: “it is unusual to pay such high
interest in loan agreements.” Such a service can empower consumers
to act, and the possibility of this action might itself deter firms from
engaging in such practices in the first place.
This potential, however, should not be overstated. First, effective
benchmarking requires finding a relevant comparison group against
which to compare. However, as personalization grows, so does the variety of contracts, so it is increasingly harder to define such a group.
Second, even if benchmarking detects disparate treatment, there is
only so much the individual consumer can do to address systemic social issues. The complexity of algorithmic decision making will often
allow firms to veil discrimination behind other seemingly neutral
factors.185
In another important sense, there is too little personalization.
When firms draft contracts, they normally only account for the needs
and characteristics of a hypothetical “reasonable consumer,” which
literature suggests is presumed white, educated, and male.186 Firms
have little incentive to improve on this standard, as meeting it will
182 Id.; see also David A. Hoffman, From Promise to Form: How Contracting Online
Changes Consumers, 91 N.Y.U L. REV. 1595, 1634–42 (2016) (detailing concerns with strategic
contract personalization). For a discussion of the origin of the hostility to standard terms, see
LLEWELLYN, supra note 27, at 362–71. R
183 See, e.g., Matthew Adam Bruckner, The Promise and Perils of Algorithmic Lenders’ Use
of Big Data, 93 CHI.-KENT L. REV. 3 (2018); Anya E.R. Prince & Daniel Schwarcz, Proxy Discrimination in the Age of Artificial Intelligence and Big Data, 105 IOWA L. REV. 1267 (2020).
184 Bruckner, supra note 183, at 29. R
185 See Andrew D. Selbst & Solon Barocas, The Intuitive Appeal of Explainable Machines,
87 FORDHAM L. REV. 1085 (2018) (discussing the inscrutability and nonintuitive nature of algorithmic models); Prince & Schwarcz, supra note 183, at 1257 n.29. R
186 See Amy H. Kastely, Out of the Whiteness: On Raced Codes and White Race Consciousness in Some Tort, Criminal, and Contract Law, 63 U. CIN. L. REV. 269, 293–94 (1994); Lu-in
Wang, Negotiating the Situation: The Reasonable Person in Context, 14 LEWIS & CLARK L. REV.
1285 (2010); Lavie v. Procter & Gamble Co., 129 Cal. Rptr. 2d 486, 498 (Cal. Ct. App. 2003).

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ensure enforcement by the courts even if the specific consumer is
atypical.187 Moreover, regulators are limited in their ability to regulate
firms’ activities. Demanding personalization is expensive, and regulators are generally wary about practices that distinguish among consumers based on race-adjacent considerations.188
Smart readers are remarkable because they offer a user-side solution to this problem. The model responds to the user’s needs, utilizing
the information provided by consumers to their benefit. This allows
the smart reader to meet granular consumer needs. Recall the examples above, illustrating how smart readers can tailor a message to a
person from a specific region of the United States or an immigrant
from a different country.189 We also noted that the technology could
personalize outputs in a way that can be intersectionally rich, thus addressing the needs of people who belong to several social subgroups.
Although regulators and courts are wary of firms personalizing contracts based on consumers’ demographics, they could relax their guard
when such personalization comes from, and serves the interest of, the
consumer.
Just as smart readers can alleviate some forms of discrimination,
they can exacerbate others. A specific concern is that firms will discriminate among consumers based on their propensity to use smart
readers. If firms can identify savvy customers who use smart readers
in advance, they can offer them better contracts. These terms will not
be equally extended to those consumers who are less likely to read the
contract. In fact, firms may purposefully offer these nonreading consumers inferior terms. In this scenario, smart readers would lead to
regressive cross-subsidies among consumer groups moving money
from poor consumer groups, who will receive low-quality terms, to
richer and more sophisticated ones, who will receive improved
terms.190
187 See, e.g., Freeman v. Time, Inc., 68 F.3d 285, 289 (9th Cir. 1995) (“[T]he reasonable
person standard is well ensconced in the law in a variety of legal contexts in which a claim of
deception is brought.” (quoting Haskell v. Time, Inc., 857 F. Supp. 1392, 1398 (E.D. Cal. 1994)));
cases cited supra note 68; see also Russell N. Laczniak & Sanford Grossbart, An Assessment of R
Assumptions Underlying the Reasonable Consumer Element in Deceptive Advertising Policy, 9 J.
PUB. POL’Y & MKTG. 85, 86 (1990) (noting that courts use the word reasonable to describe
consumers who are commonly competent and knowledgeable).
188 See Civil Rights Act of 1964, 42 U.S.C. §2000a(a) (“All persons shall be entitled to the
full and equal enjoyment of the goods, services, facilities, privileges, advantages, and accommodations of any place of public accommodation . . . without discrimination or segregation on the
ground of race, color, religion, or national origin.”).
189 See supra Section I.B.
190 See Meirav Furth-Matzkin, The Distributive Impacts of Nudnik-Based Activism, 74

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This discriminatory dynamic is predicated on the firms’ ability to
distinguish among consumers based on their actual use, or propensity
to use, smart readers. On this point, a growing industry offers scoring
services, metrics, and proxies that rank consumers on a variety of
dimensions.191 Some of these services identify in advance consumers
who are assertive, problematic, or less profitable.192 Firms embed such
metrics in their operations to determine whom to target for advertising campaigns, how long each consumer should wait on the line when
calling the company, and how much effort to exert in retaining a specific consumer.193 It is a small leap to see firms identifying consumers
based on who has, for example, installed a smart reader on their
phone or used it recently.
Perhaps consumers themselves would resolve part of this problem. Disadvantaged consumers may realize that they can get better
treatment by mimicking their better-served peers and downloading a
smart reader to their phone. Still, this solution is limited. The uninformed consumers need to be aware of the inferior treatment they
receive, which will require some understanding of the contract at
hand. Then consumers need to make the causal link between this inferior treatment and the characteristic that made them a target for
such treatment. In the age of big data, many factors could affect how
vendors treat consumers.194 Such mimicking strategies are not even
available if there is a deeper, systematic reason that distinguishes between smart reader users and nonusers. If nonusers are, for example,
technophobes, elderly, do not own a smartphone (like one of the authors), or if there are digital inclusion disparities among subgroups,
simple mimicry will not bridge this gap.195
VAND. L. REV. EN BANC 469, 471 (2021) (arguing that firms favor nudniks at the expense of
other consumer groups); Natasha Sarin, Making Consumer Finance Work, 119 COLUM. L. REV.
1519, 1529–30 (2019) (arguing for regulation against regressive cross subsidies, independently of
social welfare).
191 Arbel & Shapira, supra note 110, at 960–65. R
192 Id. at 962–63.
193 Id.
194 See Prince & Schwarcz, supra note 183; see also Arbel, supra note 165, at 148 (arguing R
that the black-box nature of algorithms can discourage gaming).
195 We noted above that many poor people in the United States own a smartphone. See
supra note 170. That said, access to technology often benefits the haves, and the concepts of R
digital inclusion and digital divide are richer and nuanced. See, e.g., Anique Scheerder, Alexander van Deursen & Jan van Dijk, Determinants of Internet Skills, Uses and Outcomes. A Systematic Review of the Second-and Third-Level Digital Divide, 34 TELEMATICS & INFORMATICS 1607
(2017).

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F. Nudging with Smart Readers
Behavioral biases and cognitive constraints are said to undermine
the ability of consumers to make prudent decisions.196 To counter
some of these biases, behavioralists often recommend using various
nudges that improve the decision-making environment.197 There are
many different types of biases, and although smart readers do not address all of them, they do seem well poised to tackle a few major
types. These include (1) cognitive overload, (2) myopia and risk discounting, and (3) price-related manipulations.
Cognitive overload describes the phenomenon where the quantity and presentation of information saturate the receiver’s processing
capacity.198 Consumer contracts contribute to a sense of cognitive
overload through their contractual bloat, high degree of complexity,
unfamiliar formatting, repetitive styling, and long contingency lists.199
The concern here is not so much that the consumer will not read the
contract per se, but rather that reading will prove futile given the expected cognitive overload.
When an individual experiences a cognitive overload, they strive
to remove it by resorting to simple heuristics, deferring decision making, or arbitrarily cutting down the decision space.200
196 For a succinct discussion, see supra Part II. Both the relevance of biases and the way to
treat them are contentious issues. See, e.g., Todd J. Zywicki, The Behavioral Economics of Behavioral Law & Economics, 5 REV. BEHAV. ECON. 439 (2018) (offering a critique).
197 The term “nudge” became famous following the influential book by RICHARD H. THALER & CASS R. SUNSTEIN, NUDGE: IMPROVING DECISIONS ABOUT HEALTH, WEALTH, AND HAPPINESS (2008). The concept of nudging has been vastly discussed, developed, employed, and
criticized. See generally, e.g., Daniel E. Ho, Fudging the Nudge: Information Disclosure and Restaurant Grading, 122 YALE L.J. 574 (2012) (arguing that the nudge of restaurant sanitation grading suffers from serious flaws); Cass R. Sunstein, Nudges That Fail, 1 BEHAV. PUB. POL’Y 4
(2017) (delineating reasons that may lead a nudge to fail and providing three possible responses
for such a failure); Lauren E. Willis, When Nudges Fail: Slippery Defaults, 80 U. CHI. L. REV.
1155 (2013) (arguing that often policy nudges in the form of defaults are unlikely to be effective).
198 Jonathan M. Landers & Ralph J. Rohner, A Functional Analysis of Truth in Lending, 26
UCLA L. REV. 711, 722 (1979) (disclosures can “overwhelm[] [the consumer] by the aggregate
mass of words and figures” and thus lead the consumer to ignore the disclosure); see Martin J.
Eppler & Jeanne Mengis, The Concept of Information Overload: A Review of Literature from
Organization Science, Accounting, Marketing, MIS, and Related Disciplines, 20 INFO. SOC’Y 325,
326 (2004).
199 Eric Posner argues that businesses sometimes offer pay-now-terms-later contracts in order to protect the consumer from cognitive overload. Eric A. Posner, ProCD v Zeidenberg and
Cognitive Overload in Contractual Bargaining, 77 U. CHI. L. REV. 1181 (2010).
200 See David M. Grether, Alan Schwartz & Louis L. Wilde, The Irrelevance of Information
Overload: An Analysis of Search and Disclosure, 59 S. CAL. L. REV. 277 (1986) (arguing that in
the presence of information overloads, consumers satisfice preferences rather than optimize);

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Consequently, a large body of research shows that cognitive overload results in poor judgment, lower accuracy, and “[g]reater tolerance of error.”201 Sellers of timeshares, for instance, are notorious for
exploiting this phenomenon by bombarding prospective buyers with
information and imposing artificially strict deadlines (“this discount
will go away in 20 minutes”).202
Smart readers can help address cognitive overload by reducing
the intensity of information. They can do so directly by summarizing
text or, even better, giving it a simple score. They can also reduce the
overload indirectly by adapting the presentation, changing the formatting and styling of the text, increasing the contrast and size of the
print, and using bullet points.203
Term optimism and myopia reflect the human tendencies to believe the contract is more favorable than it actually is and undervalue
future loss or risks.204 Smart readers can offer a useful intervention by
exploiting a countervailing bias. Consumers are said to “attach disproportionately high weight to salient attributes.”205 If that is true, the
smart reader may be able to counter optimism and myopia by making
salient issues that would otherwise be latent, such as return policies
and warranties. Such focus can make potential transactional problems
more salient and draw consumers’ awareness to the relevant risk.
Smart readers can also help with price partitioning and other
price manipulations. Sellers often partition prices by displaying the
price of a product across several categories of surcharges, such as hanLanders & Rohner, supra note 198, at 722–25 (arguing that in the presence of cognitive overload R
consumers will ignore disclosure).
201 See Peter Gordon Roetzel, Information Overload in the Information Age: A Review of
the Literature from Business Administration, Business Psychology, and Related Disciplines with a
Bibliometric Approach and Framework Development, 12 BUS. RSCH. 479, 502 (2019).
202 See Gretchen Morgenson, The Timeshare Hard Sell Comes Roaring Back, N.Y. TIMES
(Jan. 22, 2016), https://www.nytimes.com/2016/01/24/business/diamond-resorts-accused-of-usinghard-sell-to-push-time-shares.html [https://perma.cc/7RRA-ASLX]. Indeed, the Federal Trade
Commission warns against acting impulsively or under pressure when considering a timeshare
transaction, while highlighting the importance of a “cooling-off period”. See Timeshares, Vacation Clubs, and Related Scams, FTC CONSUMER INFO., https://www.consumer.ftc.gov/articles/
0073-timeshares-and-vacation-plans [https://perma.cc/RGU8-MLMK].
203 Food labeling is one important domain in which regulators attempt to reduce cognitive
overload by employing “smart disclosures” and interpretive labeling. See, e.g., Oren Bar-Gill,
Smart Disclosure: Promise and Perils, 5 BEHAV. PUB. POL’Y 238 (2021); Shmuel I. Becher,
Hongzhi Gao, Alana Harrison & Jessica C. Lai, Hungry for Change: The Law and Policy of
Food Health Labeling, 54 WAKE FOREST L. REV. 1305 (2019).
204 See Ayres & Schwartz, supra note 4 (discussing the problem of term optimism); BAR-R
GILL, SEDUCTIONBY CONTRACT, supra note 114. R
205 Pedro Bordalo, Nicola Gennaioli & Andrei Shleifer, Salience and Consumer Choice, 121
J. POL. ECON. 803, 803 (2013).

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dling, shipping, and convenience fees. Some studies show that consumers tend to underestimate the total transaction cost when firms
engage in price partitions.206 Other price manipulations include
presenting unround prices such as $2.95 or $299.99.207 If smart readers
can examine the transaction as a whole and present the final price,
rounded, they can help overcome such manipulations.
Whether smart readers will successfully debias consumers is hard
to know without testing. Over time, academics and private consumer
organizations may seek to develop such nudges and test their efficacy
in the field. At this stage, suffice it to highlight that smart readers offer
a new channel of intervention in consumer decision making.
IV. REGULATING CONTRACTS IN THE AGE OF SMART READERS
Smart readers can have a large impact on the market, even if
adoption is only modest. Some of this impact is benign—increasing
access to justice or jumpstarting term competition. But some of this
impact may be deleterious, such as the case of adversarial attacks and
discrimination. Traditionally, the common law has been slow to respond to technological advances. Although a wait-and-see regulatory
approach may be sensible in many domains, several issues do require
preparation and deliberation.208
In what follows, Section A explores the broader theoretical consequences of smart readers for the future of regulation of consumer
contracts. Section B briefly considers how courts and agencies may
use smart readers. Section C closes by examining four categories of
specific doctrinal adaptations and responses to smart readers.
A. The Challenge to Consumer Protection
Scholars offer various justifications for interventions in consumer
contracts: fairness, market failures, paternalism, choice architecture,
and empowerment—to name a few.209 Among these, the no-reading
206 See Eric A. Greenleaf, Eric J. Johnson, Vicki G. Morwitz & Edith Shalev, The Price
Does Not Include Additional Taxes, Fees, and Surcharges: A Review of Research on Partitioned
Pricing, 26 J. CONSUMER PSYCH. 105, 108–11 (2016).
207 For further analysis, see Kenneth C. Manning & David E. Sprott, Price Endings, LeftDigit Effects, and Choice, 36 J. CONSUMER RSCH. 328 (2009), and Manoj Thomas & Vicki
Morwitz, Penny Wise and Pound Foolish: The Left-Digit Effect in Price Cognition, 32 J. CONSUMER RSCH. 54 (2005).
208 See Van Loo, supra note 21, at 821 (“The task of financial stability regulators and schol-R
ars is not necessarily to predict the next crisis, or even to make the case that any trigger is likely
to cause a crisis. . . . Instead, the task is to improve risk monitoring, which includes minimizing
theoretical blind spots.”).

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problem stands out as the most common one.210 Its appeal lies in its
ability to unite fairness-minded scholars, libertarians, and welfarists,
as all are concerned with assent under conditions of informational
asymmetry.211 Many pro-consumer interventions are thus couched in
the no-reading problem.
Smart readers suggest a new way of thinking about the no-reading problem. Rather than an ethical issue that justifies legal intervention, lack of reading may be a technological challenge that is
increasingly solved. Thinking about the problem in this way offers
some new insights on persistent legal issues.
To take a particularly important example, consider the recent debate around the Draft Restatement of Consumer Contracts.212 In
2012, the American Law Institute announced a restatement project of
the law of consumer contracts.213 Scholars still debate the resulting
draft on various dimensions.214 Although the debate is far from settled, it clarified that both sides consider contract reading a fundamental problem that justifies legal intervention.215 The Reporters note, for
ment toward the proper approach to consumer contracts see, for example, RADIN, supra note 6; R
Omri Ben-Shahar, Regulation Through Boilerplate: An Apologia, 112 MICH. L. REV. 883 (2014);
Margaret Jane Radin, What Boilerplate Said: A Response to Omri Ben-Shahar (and a Diagnosis)
(Univ. Mich. Pub. L. & Legal Theory Rsch., Paper Series, Paper No. 392, L. & Econ. Rsch.
Paper Series, Paper No. 14-007, 2014), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=
2401720 [https://perma.cc/ST65-EA7A].
210 See generally Ayres & Schwartz, supra note 4. R
211 Oren Bar-Gill, The Behavioral Economics of Consumer Contracts, 92 MINN. L. REV.
749 (2008); Richard A. Epstein, The Neoclassical Economics of Consumer Contracts, 92 MINN. L.
REV. 803 (2008).
212 DRAFT RESTATEMENT 2019, supra note 4. See discussion supra note 33, for the debate. R
213 DRAFT RESTATEMENT 2019, supra note 4, at xiii. R
214 See, e.g., Gregory Klass, Empiricism and Privacy Policies in the Restatement of Consumer Contract Law, 36 YALE J. ON REG. 45 (2019); Adam J. Levitin, Nancy S. Kim, Christina L.
Kunz, Peter Linzer, Patricia A. McCoy, Juliet M. Moringiello, Elizabeth A. Renuart & Lauren
E. Willis, The Faulty Foundation of the Draft Restatement of Consumer Contracts, 36 YALE J. ON
REG. 447 (2019); see also Mark E. Budnitz, The Restatement of the Law of Consumer Contracts:
The American Law Institute’s Impossible Dream, 32 LOY. CONSUMER L. REV. 369 (2020); Nancy
S. Kim, Ideology, Coercion, and the Proposed Restatement of the Law of Consumer Contracts, 32
LOY. CONSUMER L. REV. 456 (2020).
215 DRAFT RESTATEMENT 2019, supra note 4, §2 cmt. 1 (“This Section . . . operates in a R
reality in which consumers are . . . unlikely to read and exercise meaningful informed consent to
the non-core standard contract terms.”). In a separate publication, the Restatement drafters
note, for example, that “[t]he proliferation of lengthy standard-term contracts, mostly in digital
form, makes it practically impossible for consumers to scrutinize the terms and evaluate them
prior to manifesting assent.” See Oren Bar-Gill, Omri Ben-Shahar & Florencia Marotta-Wurgler,
The American Law Institute’s Restatement of Consumer Contracts: Reporters’ Introduction, 15
EUR. REV. CONT. L. 91, 92 (2019). The drafters further state that “[b]ecause of the imbalance
between businesses and consumers, the application of contract law’s general rules of mutual
assent alone are not likely to level the playing field.” Id. at 93.

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example, that “lengthy standard forms” are “unlikely to [be] read” by
consumers,216 and that it is “irrational and infeasible” for consumers to
read such contracts.217 On this basis, the Reporters offered a liberal
approach to striking down boilerplate.218 But because the Draft also
proposed a relatively relaxed approach to formation, it was criticized
for “jettison[ing] meaningful assent to contract.”219
Beyond the general approach, reading problems also bear on specific arrangements in the Draft. In particular, the Draft takes a narrow
approach to merger clauses:
Because consumers are not likely to notice, read, or understand the effect of such merger clauses, they do not control
the conclusion of whether the standard contract terms constitute a partially or completely integrated agreement, and thus
do not preclude a finding that the standard contract terms do
not constitute the parties’ final expression of a particular
matter.220
If smart readers can offer a technological solution to the problem
of reading, what remains of these justifications? As smart readers
grow in sophistication and accuracy, they raise doubts as to whether
the Draft and other legal measures are future-proof.221 Indeed, if
smart readers reach this stage, it may be more effective to focus regulatory efforts on increasing adoption rates than to set mandatory rules
and enforcement mechanisms. To be sure, even if smart readers can
solve the reading problem, they will not necessarily address other
market and reputational failures, so they do not necessarily portend
216 DRAFT RESTATEMENT 2019, supra note 4, at 3. R
217 Id. at 1.
218 Id. §5 cmt. 1 (“Because consumers rarely read or review the non-core, standard contract terms . . . the doctrine of unconscionability is a primary tool against the inclusion of intolerable terms in the consumer contract.”).
219 Levitin et al., supra note 214, at 452. A previous version of the Restatement termed this R
tradeoff as a “Grand Bargain.” Id.
220 DRAFT RESTATEMENT 2019, supra note 4, §8 illus. 3. R
221 For example, the so-called “Schumer Box” requires credit card companies to disclose in
an accessible, unified, and transparent way the costs of a credit card. See 12 C.F.R. §226.5
(2020). Another key example is the disclosure of nutritional information on packed food, which
is prescribed in great detail. See 21 U.S.C. §343; 21 C.F.R. §101.9 (2020) (implementing regulations). In the context of warranties, the Magnuson-Moss Warranty Act, 15 U.S.C. §§2301–2312,
mandates that a supplier who offers any warranty may not disclaim any implied warranties. Id.
§2308. The Act was enacted because of the concern that “[t]he bold print giveth and the fine
print taketh away.” H.R. REP. NO. 93-1107, at 24 (1974). Likewise, it has also been noted that
“[t]here is substantial evidence that at the time of the sale the purchaser of a major appliance
does not understand the nature and extent of the protection provided by the manufacturer’s
warranty or of the obligation under the warranty of the manufacturer or of the retailer.” Id. at
27–28.

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the end of pro-consumer legal measures. Still, to the extent smart
readers can effectively address the no-reading problem, the current
reliance on reading as a source of justification might become dated in
the coming years.
B. Courts and Agencies
Throughout the analysis, we focused on consumers utilizing smart
readers. Although we can only adumbrate the point, it is also worth
shifting focus and considering how courts and agencies may benefit
from smart readers.222
Some commentators recently expressed dissatisfaction with standard interpretative approaches, arguing that courts rely too heavily on
introspection and classic dictionaries to identify the “plain meaning”
of text.223 Although dictionaries record definitions, they abstract important clues about the true meaning of a word from linguistic context
and frequency of usage. One proposal in this context is the incorporation of “corpus linguistics” into legal interpretation—i.e., examining
linguistic usage data obtained from the processing of large corpora of
texts. Although this approach shows promise in producing greater
awareness to nuances of meaning, it can be unwieldy to use, especially
by judges not trained in linguistic methodologies.
Smart readers—and language models more generally—make the
implementation of such interpretative approaches straightforward, objective, and predictable. Rather than having a human judge read
through thousands of instances of how parties use a given term, a language model can assign probabilities. It can say, hypothetically, that
the term “chicken” is used 78% of the time in the context of the general genus, 21% of the time in the context of a broiler, and only 1% in
the context of a stewing chicken.224 When the frequency of use is at
issue, such data can become pivotal. Smart readers make the task
much more structured and accurate.
Agencies can also productively employ smart readers. They can
use them to sift through the contracts commonly used in a given sector
and identify problematic terms. For instance, if the agency seeks to
police privacy terms, it can set its smart readers to process current
privacy policies and flag offensive, suspicious, or irregular terms. Al222 Regulators and agencies are increasingly considering the adoption of digital tools to
solve policy problems. See supra note 35 and accompanying text. R
223 See Mourtisen, supra note 36, at 1340. R
224 See Frigaliment Importing Co. v. B.N.S. Int’l Sales Corp., 190 F. Supp. 116 (S.D.N.Y.
1960). The statistics used here are hypothetical.

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though smart readers will not be perfectly precise, the agency can still
conserve significant resources by channeling its limited resources to
flagged terms.225
C. Regulatory and Doctrinal Responses
The questions we discussed so far were, in a sense, at the wholesale level—considering the broad implications of smart readers. At
the retail level, questions remain on the fitness of doctrine developed
over the centuries to the age of smart readers. In this Section, we consider the ability of doctrine, courts, and agencies to protect consumers
from related risks and properly develop contract law.
1. Allocation of Error Costs
Because smart readers will make mistakes,226 and because consumers may suffer harm from relying on these mistakes, it is important to critically consider the expected response of standard common
law doctrines to such mistakes. For concreteness, consider a buyer
who purchases a television because the smart reader made them erroneously believe that the seller offers hassle-free returns; a park visitor
who is misled by the smart reader into believing that the park owner is
responsible for injuries in the park; or a worker mistakenly foregoing
his employee status and agreeing to be an independent contractor.
Tentatively assume that these mistakes are innocent—that is, they
arise from a smart reader error, and the drafter is acting in good
faith.227 What should the law do in such cases? How should the risk of
error be allocated between the contracting parties? Can the consumers successfully argue that their assent was compromised due to the
reliance on the smart reader?
Smart reader error may implicate the company that produced it,
under either a contract theory or tort liability for defective products.
Realistically, however, recourse against the producer is likely to be
very limited. One reason is the high likelihood of liability waivers in
the license agreements. Another is that courts may shy away from assigning liability in such cases in an attempt to encourage the development of smart readers.
Instead of the producer, the disappointed buyer may seek to
reach the seller. Here as well, standard doctrines seem to offer little in
225 See Arbel, supra note 165, at 147–51 (exploring the use of AI in detecting suspicious R
cases).
226 See supra Section III.B.
227 On the possibility of bad faith errors, see infra Section IV.C.3.

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the way of redress. The doctrine of mistake involves a wrong belief
regarding a basic assumption of the contract that has a material effect.228 The difficulty is that courts resist applying this doctrine to mistaken beliefs regarding the content of the contract itself.229 A different
kind of difficulty arises in the case of misrepresentation,230 as the
drafting party is not the source of the mistaken understanding, and the
smart reader cannot be easily understood to be that party’s agent.231
This leaves the doctrine of misunderstanding, which does involve
divergent interpretations of the contract.232 But misunderstanding is
too weak of a hook to hang anything valuable.233 Courts avoid finding
misunderstandings using tools of interpretation234 and through the liberal application of the duty to read.235 This general difficulty is amplified in the context of smart readers, as the source of the mistaken
understanding may be the language model, rather than the contract.236
This specific difficulty can be overcome only if the seller has reason to
know of such a misunderstanding.237 Overall, then, standard doctrines
228 RESTATEMENT (SECOND) OF CONTS. §151 (AM. L. INST. 1981) (“A mistake is a belief
that is not in accord with the facts.”).
229 See, e.g., Eric Rasmusen & Ian Ayres, Mutual and Unilateral Mistake in Contract Law,
22 J. LEGAL STUD. 309, 310 (1993) (“[J]udicial excuse for either unilateral or mutual mistake is
relatively rare . . . .”).
230 See generally RESTATEMENT (SECOND) OF CONTS. §164 (AM. L. INST. 1981) (explaining
when misrepresentation makes a contract voidable).
231 26 RICHARD A. LORD,WILLISTONON CONTRACTS §69:14 (4th ed. 2021).
232 RESTATEMENT (SECOND) OF CONTS. §20 cmt. c (AM. L. INST. 1981). For recent applications of the doctrine to invalidate contracts, see Cont’l Warranty, Inc. v. Warner, 108 F. Supp. 3d
250, 254 (D. Del. 2015), and Brooks v. Rosebar, 210 A.3d 747, 752 (D.C. 2019).
233 See Daniel P. O’Gorman, The Restatement (Second) of Contracts’ Reasonably Certain
Terms Requirement: A Model of Neoclassical Contract Law and a Model of Confusion and Inconsistency, 36 U. HAW. L. REV. 169, 199 (2014) (“[M]ost misunderstandings in fact will not
result in indefiniteness in law.”).
234 See, e.g., RESTATEMENT (SECOND) OF CONTS. §20 reporter’s note (AM. L. INST. 1981)
(distinguishing between “problem[s] of interpretation of key terms and the much less common
question whether” there was a misunderstanding).
235 See, e.g., Anderson v. Equitable Life Assurance Soc. of the U.S., 248 F. Supp. 2d 584,
590–91 (S.D. Miss. 2003) (“Mississippi law creates a duty on contracting parties to read their
contracts, and imputes the knowledge of that contract to the parties. . . . The court will generally
not consider prior oral agreements, misunderstandings between the parties, or any other form of
parol evidence.”). One exception is Cappalli v. BJ’s Wholesale Club, Inc., 904 F. Supp. 2d 184
(D.R.I. 2012). Here the court concluded that because the contract had conflicting terms in it, the
misunderstanding could not have been resolved by the reading of the contract. Id. at 191–92.
236 According to RESTATEMENT (SECOND) OF CONTS. §20(2) (AM. L. INST. 1981), courts
can enforce the contract as understood by the innocent party, if the other party had reason to
know of the misunderstanding.
237 See id. This rule of “negligent manifestation of assent,” id. §20 cmt. d, is rarely used.
For one example, see Pope v. Gap, Inc., 961 P.2d 1283, 1287 (N.M. Ct. App. 1998).

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would allocate the entirety of the risk of smart reader error to the
consumer.
A deeper question is whether sellers should be made responsible
for harms resulting from smart readers.238 The answer to this question
depends on several factors. To begin, if the smart reader provides a
mistaken output, then it may be that the consumer’s most effective
redress should come from the producer of the smart reader. In autonomous driving, for example, there has been a similar push to move
from personal liability for the accident to producer’s liability for faulty
autonomous driving technology.239 Assigning liability to the producer
is appealing in the sense that it can encourage producers to improve
their products or properly warn users. On the other hand, such solutions can stunt development in the field and place barriers to entry.
Additionally, producer liability rules can increase the cost of smart
readers and chill adoption rates, thus depressing the positive spillovers
of smart readers.240
Assuming the buyer has no recourse against the producer of the
smart reader, the allocation of responsibility between the buyer and
the seller becomes a question of who is in a better position to avoid
the “legal accident” of contractual misunderstanding. On the one
hand, the consumer may appear to be ideally situated: the consumer is
the party that harbors a misunderstanding and thus can solve it by
reading the contract. The scholarship around the no-reading problem,
however, suggests that this may be a facile assumption and that the
consumer’s ability to prevent the accident is limited.241 On the other
hand, sellers can control the risk of an accident through proactive disclosures, at least of key terms. Still, sellers are also not ideally situated
to prevent the accident, as they do not choose the app, have no control over how or when the consumer uses it, and are not privy to its
outputs.
Overall, we do not find a compelling reason why the law should
assign liability exclusively to one side of the transaction. This leads us
238 For an investigation of the allocation of risks for error codes, see Shaanan Cohney &
David A. Hoffman, Transactional Scripts in Contract Stacks, 105 MINN. L. REV. 319 (2020).
239 See, e.g., Alexander B. Lemann, Autonomous Vehicles, Technological Progress, and the
Scope Problem in Products Liability, 12 J. TORT L. 157 (2019) (arguing that the relative safety of
autonomous vehicles should not suggest that their manufacturers are immune from potential
liability when their faults cause injury).
240 As noted in supra Section III.B, positive spillovers are expected even when AI smart
readers are fairly inaccurate. Insistence on accuracy through tort liability for mistakes may thus
be disadvantageous.
241 See supra Part II.

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to consider loss allocation rules, which are key in the case of hard to
prevent accidents. Here, we think the common law solution to the noreading problem is instructive. Courts assign liability for most of the
terms to consumers, but sellers can enforce certain special terms only
if they are conspicuously disclosed. This logic can be transferable to
the context at hand by requiring sellers to disclose key terms in a
“smart reader-friendly” manner as a condition for their enforcement.
We can facilitate such a solution by adapting the meaning of conspicuous to the age of smart readers; by a stronger version of the contra
proferentem rule; or by reviving the mostly defunct duty to warn
under section 211 of the Restatement (Second) of Contracts.242 Overall, the goal should be to navigate the incentives of both parties while
minimizing the costs of errors.
2. The Duty to Read
If smart readers are cheap and accessible, courts may find it natural to expect consumers to use them. Based on this expectation, courts
may expand the doctrine of the duty to read. Such a move can be
premature.
Courts have long imposed a misnamed “duty” to read contracts.243 Under this rule, courts will enforce the terms of an unread
contract as long as the consumer had a proper opportunity to read the
contract terms.244 Courts assume that the duty encourages consumers
to read terms and avoid strategic claims of unread terms.245 The concern here, however, is that courts and legislatures will come to excessively rely on smart readers by expanding the duty to read. Once
smart readers become common, courts might make increasingly strong
242 RESTATEMENT (SECOND) OF CONTS. §211(3) (AM. L. INST. 1981) (“Where [a] party has
reason to believe that [another] party manifesting . . . assent [to a standard form] would not do
so if he knew that the writing contained a particular term, the term is not part of the agreement.”); see also Kar & Radin, supra note 31, at 1202. R
243 See, e.g., Ayres & Schwartz, supra note 4, at 548 n.10 (citing case law applying the duty R
to read in the context of consumer contracts); Wayne R. Barnes, Toward a Fairer Model of
Consumer Assent to Standard Form Contracts: In Defense of Restatement Subsection 211(3), 82
WASH. L. REV. 227, 230 (2007); Charles L. Knapp, Is There a “Duty to Read”?, 66 HASTINGS L.J.
1083, 1085 (2015).
244 See, e.g., Rustad & Koenig, supra note 47, at 1453 (“U.S. courts have expanded the duty R
to read . . . to the world of electronic boilerplate . . . .”).
245 See Omri Ben-Shahar, The Myth of the ‘Opportunity to Read’ in Contract Law, 5 EUR.
REV. CONT. L. 1, 7 (2009) (“Rather, [the duty] is a method to shift the burden of information
acquisition to the passive party.”); Korobkin, supra note 5, at 1269 (“If buyers could preserve the R
right to challenge ex post any contract term of which they were unaware ex ante, they would
have a perverse incentive to avoid learning the content of all terms.”).

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assumptions on the ability of the specific consumer to read and understand the contract.
A few considerations ground the possibility of premature expansion of the duty to read. First, firms and other repeat players may
advocate the courts to adopt such a policy. The technology is already
sufficiently impressive to mislead an inexperienced person into believing that it is more effective than it actually is. Second, courts have not
demonstrated technological acuity and agility in the context of browsewraps and clickwraps, still struggling to articulate clear rules two
decades after these have become household issues.246 Third, courts
and legislators may be tempted to use the duty to read strategically as
a means of encouraging the adoption of smart readers.
If there is a wide gap between judicial expectations and technological or consumer realities, such judicial insistence will backfire.
Having a more muscular duty to read without a commensurate enhancement in the actual reading and understanding of contracts can
prove deleterious. Most worryingly, if access to smart readers is unequally distributed so that only certain classes of consumers benefit
from them, such a judicial shift can lead to regressive cross-subsidies
that exacerbate inequalities.247
In conclusion, we urge courts and policymakers to resist attempts
to jump the gun and prematurely expand the duty to read. We recognize that this will somewhat diminish the incentive to use smart readers. Nevertheless, we consider this a fairly small price to pay for
gradual development and a more informed policy.
3. The Problem of Adversarial Attacks
Adversarial attacks are a slippery problem. Detection is likely to
be exceedingly difficult. Both the panda and stop sign examples
demonstrated how ever-so-slight manipulation of pixels could mislead
a sophisticated AI model.248 In the context of contracts and other written documents, such attacks can wear a dizzying array of forms. These
may include deliberate manipulation of the spacing, font choice, the
order of words in a sentence, font size and color, choice of synonyms,
register, and document margins to trick AI models into producing a
246 See, e.g., Budnitz, supra note 214, at 415 (“[D]ue to developments in technology, the R
environment where online consumer transactions occur is in constant flux.”).
247 See, e.g., Mark Lloyd, The Digital Divide and Equal Access to Justice, 24 HASTINGS
COMMC’NS & ENT. L.J. 505, 527–30 (2002) (explaining that unequal access to technology could
lead to exacerbated inequalities in the justice system).
248 See supra Section III.B.

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desirable outcome. Humans can hardly be trusted to detect such manipulations, so one might hope that smart readers can be trained for
this purpose. But this is precisely the problem. Detecting manipulation may require judgment about the correct classification of the contract, which is what the models lack.
Beyond the problem of detection, proving that a given error is
deliberate will be extremely difficult. Thus, even if one suspects that
firms calculate the choice of font and spacing, proving that this was
made to deliberately confuse smart readers will require strong evidence. Contract drafters have broad latitude over the design of their
agreements, and, in practice, drafters employ different designs for reasons that are entirely innocuous.249
The economic theory of enforcement suggests a solution to the
problem of hard-to-detect violations: the use of large fines and punitive damages that compensate for the possibility that violations will go
undetected.250 Unfortunately, it will be difficult to apply these prescriptions to adversarial attacks. Contract law is averse to the use of
punitive damages,251 and tort-based theories of fraud will also be limited.252 Beyond the problem of detection and proof, there are also
practical limitations on the feasible size of penalties that can be effectively levied. Perhaps fraud-based challenges can provide some deterrence, especially against large firms, but they are not likely to provide
a comprehensive solution.
249 See, e.g., Hoffman, supra note 5 (discussing firms who use accessible and humorous R
language alongside more formal and traditional form contract terms).
250 See A. Mitchell Polinsky & Steven Shavell, The Economic Theory of Public Enforcement of Law, 38 J. ECON. LITERATURE 45, 67 (2000). The DRAFT RESTATEMENT 2019, supra
note 4, §6, suggests making terms entered through deceptive acts unenforceable, reflecting the R
common law’s standard of misrepresentation. See RESTATEMENT (SECOND) OF CONTS. §164
(AM L. INST. 1981). This remedy, however, offers little in the way of deterrence if violations are
hard to detect.
251 See, e.g., O’Gilvie v. United States, 519 U.S. 79 (1996); Honda Motor Co. v. Oberg, 512
U.S. 415 (1994); 5 ARTHUR LINTON CORBIN, CORBINON CONTRACTS§1077 (1964); 11 WILLISTONON CONTRACTS 209 (W. Jaegered., 3d ed. 1968); RESTATEMENT (SECOND) OF CONTS. §355
(AM. L. INST. 1981) (“Punitive damages are not recoverable for a breach of contract unless the
conduct constituting the breach is also a tort for which punitive damages are recoverable.”);
U.C.C. §1-305 cmt. 1 (AM. L. INST. & NAT’L CONF. COMM’RSON UNIF. STATE L. 2020) (stating
that contractual remedies “do not include consequential or special damages, or penal damages”).
But see Timothy J. Sullivan, Punitive Damages in the Law of Contract: The Reality and the Illusion of Legal Change, 61 MINN. L. REV. 207 (1977) (arguing that punitive damages are more
common in contract law than it seems).
252 See William S. Dodge, The Case for Punitive Damages in Contracts, 48 DUKE L.J. 629
(1999) (reporting that thirty-nine states do not allow punitive damages for contract breach unless
the plaintiff can establish the existence of an independent tort).

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Another possible solution is burden-shifting based on statistical
indicia of wrongdoing. When courts are faced with clear signs of
wrongdoing but with limited ability to prove it, they use doctrines
such as res ipsa loquitur to shift the burden of proof.253 If it turns out
that a contract leads a sample of smart readers to the wrong interpretation, courts can shift the burden to the defendant. It will then be the
defendant who would have to prove that such errors are not intentional, or they will be liable to meet the interpretation suggested by
the consumer.
Though having some initial appeal, this solution is also not without difficulty. The problem is that smart readers will always have some
degree of technical errors that are not due to the drafter, but rather to
the state of technology. Making sellers liable for smart reader errors
imposes a considerable cost on them, though their ability to avoid it is
limited because they have little to say concerning the design and implementation of smart readers.
One last approach is ongoing regulatory monitoring, for instance,
by the Consumer Financial Protection Bureau or the Federal Trade
Commission.254 Enforcement agencies could utilize their systems of
smart readers to identify instances in which document style and
formatting raise suspicion of strategic manipulation. To be sure, this
will also not be a perfect solution, because adversarial examples can
be invisible to smart readers as well. Regardless, it is a step toward
resolving a recognizably slippery problem, and it has the merit of inviting consumer organizations and enforcement agencies to be active
in this domain.
4. Bias and Discrimination
Firms wield broad latitude in personalizing the content and
formatting of contracts, though limited exceptions on some forms of
discrimination exist.255 This leeway reflects a long-held view that treats
253 See generally J. Shahar Dillbary, The Case Against Collective Liability, 62 B.C. L. REV.
391, 392 (2021). A more creative approach is Professor Lahav’s concept of a so-called “knowledge remedy.” Lahav suggests that in some cases where causality is hard to prove, courts would
order the defendant to fund research that would determine causality. See Alexandra D. Lahav,
The Knowledge Remedy, 98 TEX. L. REV. 1361, 1387 (2020).
254 These two agencies have different regulatory approaches. See Rory Van Loo, Regulatory Monitors: Policing Firms in the Compliance Era, 119 COLUM. L. REV. 369, 393–95 (2019).
255 See, e.g., Civil Rights Act of 1964 §§701–718, 42 U.S.C. §§2000e–2000e-17 (prohibiting
discrimination, among other things, on the basis of race, religion, sex, and national origin); Equal
Credit Opportunity Act §§1002.1–1002.16, 15 U.S.C. §§1691–1691f; Fair Housing Act
§§800–818, 42 U.S.C. §§3601–3619; see also Americans with Disabilities Act of 1990
§§101–514, 42 U.S.C. §§12101–12213 (prohibiting discrimination on the basis of disability).

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standard form contracts with suspicion and personalized agreements
favorably. The emergence of big data and personalization at scale
should flip this presumption on its head.256
Today, firms can tailor contracts to specific consumers that can be
highly harmful. A particular concern for our purposes arises if firms
choose to offer consumers who use smart readers better terms than
terms offered to consumers who do not use them. We noted how such
disparate treatment could lead to regressive cross-subsidies among the
consumer groups. An even more pressing concern arises if the propensity to use smart readers is correlated with race or other social characteristics. All in all, such discrimination can eliminate the positive
spillovers of smart readers.257
One possible route to addressing this discrimination is through
the various laws prohibiting unfair and deceptive acts and practices.258
At the federal level, an unfair practice is one that “is likely to cause
substantial injury to consumers which is not reasonably avoidable by
consumers themselves and not outweighed by countervailing benefits
to consumers or to competition.”259 The common standard at the state
level is the one outlined in FTC v. Sperry & Hutchinson Co., which
includes an examination as to “whether the practice . . . causes substantial injury to consumers.”260 The argument here would be that certain personalization practices cause a “substantial injury” to those
consumers who receive inferior terms that they cannot reasonably
avoid, which are not outweighed by other benefits. The problem is
that commentators debate whether market segmentation ever meets
this standard.261
An alternative is to consider this practice deceptive. Offering inferior terms to consumers who are most likely to be ignorant about
them may be deemed deceptive. This is especially so, if these consumers have developed expectations based on the treatment received by
256 See supra notes 181–84 and accompanying text. R
257 See supra Section III.E.
258 15 U.S.C. §45 (empowering the Federal Trade Commission to prevent certain unfair
and deceptive acts). Entities regulated by the Consumer Financial Protection Bureau are subject
to 12 U.S.C. §5531 (“[p]rohibiting unfair, deceptive, or abusive acts or practices”). At the state
level, there are differences in scope but “[e]very state . . . prohibit[s] deceptive practices, and
many . . . also prohibit unfair and unconscionable practices.” ADAM J. LEVITIN, CONSUMER
FINANCE: MARKETSAND REGULATION 81 (2018).
259 15 U.S.C. §45(n).
260 405 U.S. 233, 244 n.5 (1972). See generally LEVITIN, supra note 258, at 82–83. R
261 See Dennis D. Hirsch, That’s Unfair! Or Is It? Big Data, Discrimination and the FTC’s
Unfairness Authority, 103 KY. L.J. 345, 353–57 (2014–2015) (arguing that the segmentation based
on big data can constitute an unfair practice); cf. Bruckner, supra note 183, at 42–47. R

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users who employ smart readers.262 The standard here is less demanding and may be supported by a showing of a misleading material omission.263 Nonetheless, even this argument requires establishing the
existence of an actual misperception, and proof of this may not always
be available.
We recognize that limiting the freedom of contract raises difficulties. Personalization serves many benign purposes, and we do not consider a blanket prohibition desirable. But in the particular case of
smart reader-based discrimination, there are pressing concerns about
potential racial discrimination, regressive cross-subsidies, and the
elimination of positive spillovers. Admittedly, the balance of these
considerations is a matter of values as much as it is a matter of empirics. At best, we can raise these issues to public awareness and hope
that future research and debate will shed more light on the way the
law should treat this kind of discrimination. However, we feel confident in saying that this complex issue comes with a deadline. Once
firms start collecting data on smart reader usage and tailor treatment
on this basis, it will not be easy to undo the results. Here again, an
ounce of precaution now is worth a pound of cure later.
262 FTC POLICY STATEMENT ON DECEPTION (Oct. 14, 1983), https://www.ftc.gov/system/
files/documents/public_statements/410531/831014deceptionstmt.pdf [https://perma.cc/8LWQFSR8] (“In some circumstances, the Commission can presume that consumers are likely to reach
false beliefs . . . because of an omission.”).
263 Id. (agency’s interpretation); see also LEVITIN, supra note 258, at 81–82. R

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CONCLUSION264
264 Written by GPT-3. Screenshot [10] (on file with authors).