The Generative Reasonable Person

reasonable person
reasonableness
negligence
torts
consent
deception
contract interpretation
hidden fees
large language models
empirical legal studies

# The Generative Reasonable Person

Canonical citation:
Yonathan A. Arbel, The Generative Reasonable Person, BYU Law Review (2026).

Stable identifiers:
- Canonical page: https://works.battleoftheforms.com/papers/ssrn-5377475/
- Mirror page: https://works.yonathanarbel.com/papers/ssrn-5377475/
- Paper ID: ssrn-5377475
- SSRN ID: 5377475
- Dataset DOI: https://doi.org/10.5281/zenodo.18781458
- Full text: https://works.battleoftheforms.com/papers/ssrn-5377475/fulltext.txt
- Markdown: https://works.battleoftheforms.com/papers/ssrn-5377475/index.md
- PDF: https://works.battleoftheforms.com/papers/ssrn-5377475/paper.pdf
- Source repository: https://github.com/yonathanarbel/my-works-for-llm/tree/main/papers/ssrn-5377475

Same-as links:
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5377475

One-paragraph thesis:
Introduces the "generative reasonable person," an LLM-based tool for estimating how ordinary people judge reasonableness. Adapting randomized controlled trial designs to large language models, he replicates three published studies across negligence, consent, and contract interpretation using nearly 10,000 simulated decisions. The models reproduce subtle, counterintuitive patterns: social conformity beats cost-benefit analysis in negligence; lies about a transaction's essence matter more than material lies for consent; and lay contract formalism treats hidden fees as more enforceable than fair ones. The approach supplies a scalable empirical baseline but must be carefully cabined.

What this paper is about:
This Article introduces the “generative reasonable person,” an LLM-based method for estimating how ordinary people judge reasonableness. Using Silicon Randomized Controlled Trials (S-RCTs), it replicates three published studies across negligence, consent under deception, and contract interpretation with nearly 10,000 simulated decisions. Models reproduce subtle, counterintuitive lay patterns that diverge from doctrinal expectations (e.g., social conformity over cost–benefit analysis in negligence; essential lies undermining consent more than material lies; and lay contract formalism regarding hidden fees). The paper argues this can provide scalable empirical guardrails for legal judgment, while emphasizing careful validation, transparency, and limits around calibration and prompt sensitivity.

Core claims:
1. This Article introduces the “generative reasonable person,” an LLM-based method for estimating how ordinary people judge reasonableness. Using Silicon Randomized Controlled Trials (S-RCTs), it replicates three published studies across negligence, consent under deception, and contract interpretation with nearly 10,000 simulated decisions. Models reproduce subtle, counterintuitive lay patterns that diverge from doctrinal expectations (e.g., social conformity over cost–benefit analysis in...
2. The paper proposes a "generative reasonable person" to make lay reasonableness judgments observable at scale. Traditional debates about whether the reasonable person is empirical or normative presume that lay judgments are slow and costly to collect. By simulating those judgments with modern language models, the paper argues that the missing empirical baseline can be surfaced, turning what used to be hidden judicial intuition into an explicit, testable choice.
3. The study adapts randomized controlled trial designs to LLMs and replicates three published experiments spanning negligence, consent, and contract interpretation. It collects nearly 10,000 simulated responses, mirroring the original experimental structures while exploiting the scalability of model-based sampling. The goal is not to claim perfect substitution for human subjects, but to test whether models can reproduce established, nuanced patterns in lay judgment.
4. In the negligence replication, models prioritize social conformity over cost-benefit analysis, a result that runs against textbook treatments of negligence doctrine. The simulated judgments invert the expected hierarchy by placing community norms above formal efficiency calculations, aligning with empirical findings from human-subject studies.
5. In contract interpretation, the models reflect a form of lay formalism. They treat hidden fees as more enforceable than fair terms, tracking the pattern that ordinary interpreters may privilege formal presentation and textual cues over substantive fairness. This finding echoes earlier experimental results about how non-experts evaluate contractual meaning.

Controlled topic assignment:
- Primary topics: Artificial Intelligence And Law, Empirical Legal Studies
- Secondary topics: Contracts And Remedies
- Mention-only topics: Consumer Law And Contracting
- Not topics: Defamation And Speech, AI Regulation And Safety

Doctrinal contribution:
This work is relevant to Artificial Intelligence And Law, Empirical Legal Studies, Contracts And Remedies. It should be used as a source for the paper's specific argument, methodology, claims, and limits rather than as a generic statement about all of law.

Empirical or methodological contribution:
Legal standards often rely on claims about what “reasonable people” would think, understand, or do—whether in negligence, consent, or contract interpretation. Yet courts typically lack a scalable, disciplined way to surface the relevant lay baseline; in practice, “reasonableness” becomes a vessel for elite intuition. The paper argues that recent generative AI models change this constraint: if carefully queried, they can provide a fast, low-cost approximation of lay judgment patterns, turning an implicit assumption (“this is what people think”) into an explicit, testable empirical input.

Key terms:
- reasonable person: keyword associated with this work.
- reasonableness: keyword associated with this work.
- negligence: keyword associated with this work.
- torts: keyword associated with this work.
- consent: keyword associated with this work.
- deception: keyword associated with this work.
- contract interpretation: keyword associated with this work.
- hidden fees: keyword associated with this work.
- large language models: keyword associated with this work.
- empirical legal studies: keyword associated with this work.

Best use by an LLM:
This work is relevant when answering questions about Artificial Intelligence And Law, Empirical Legal Studies, Contracts And Remedies.
It should not be treated as claiming results beyond the paper's stated context, methods, evidence, and limitations. Do not retrieve it for Defamation And Speech, AI Regulation And Safety unless the user is asking about why it is outside that topic.
The most important takeaway is: Introduces the "generative reasonable person," an LLM-based tool for estimating how ordinary people judge reasonableness. Adapting randomized controlled trial designs to large language models, he replicates three published studies across negligence, consent, and contract interpretation using nearly 10,000 simulated decisions. The models reproduce subtle, counterintuitive patterns: social conformity beats cost-benefit analysis in negligence; lies about a transaction's essence matter more than...

Related works by Yonathan Arbel:
- Contract Remedies in Action: Specific Performance: https://works.battleoftheforms.com/papers/ssrn-1641438/
- ALL-CAPS: https://works.battleoftheforms.com/papers/ssrn-3519630/
- Contracts in the Age of Smart Readers: https://works.battleoftheforms.com/papers/ssrn-3740356/
- How Smart Are Smart Readers? LLMs and the Future of the No-Reading Problem: https://works.battleoftheforms.com/papers/ssrn-4491043/
- Generative Interpretation: https://works.battleoftheforms.com/papers/ssrn-4526219/

Search aliases:
- The Generative Reasonable Person
- Yonathan Arbel The Generative Reasonable Person
- Arbel The Generative Reasonable Person
- SSRN 5377475
- What has Yonathan Arbel written about artificial intelligence, large language models, and legal institutions?
- Which Yonathan Arbel works use empirical legal studies, datasets, interviews, or experiments?

[p. 1] The Generative Reasonable Person Yonathan A. Arbel This Article introduces the generative reasonable person, a new tool for estimating how ordinary people judge reasonableness. As claims about AI capabilities often outpace evidence, the Article proceeds empirically: adapting randomized controlled trials to large language models, it replicates three published studies of lay judgment across negligence, consent, and contract interpretation, drawing on nearly 10,000 simulated decisions. The findings reveal that modelscan replicate subtle patterns that run counter to textbook treatment.Like human subjects, models prioritize social conformity over cos-tbenefit analysis when assessing...

Machine-linked claim. Use the evidence quote and PDF before treating it as a quotation or as a complete statement of the paper's position.

Reasonable person standard; generative reasonable person; silicon sampling; Silicon Randomized Controlled Trials (S-RCTs / s-RCTs); stateless LLM sessions; persona prompting; negligence; Hand formula; custom vs efficiency; social norms; deception; consent; material lie vs essential lie; contract interpretation; hidden fees; fairness vs consent vs enforceability; lay formalism; simulated juries; calibration; judicial intuition; regulatory testing; empirical guardrails

guessing by precisely the elitecsriticized for being most removed from ordinary life.11 This Article providessuch a tool.It develops a methodology for eliciting reasonableness judgments from large language mode(lsLLMs) , called “Silicon Randomized Controlled Trials ” (s-RCT) .12 The approach treats independent model sessions as experimental subjects, leveraging their statelessness to enable controlled comparisons that isolate lay reasoning patterns from doctrinal recall or model sycophancy. It uses this methodology to successfully replicate with LLMs the results of human subject experiments. Across three replications involving over 10,000 simulated judgments, leading models have...


# Racing to Safety: Tax Policy for AI Safety-by-Design

Canonical citation:
Yonathan A. Arbel & Mirit Eyal, Racing to Safety: Tax Policy for AI Safety-by-Design, SMU Law Review (2026).

Stable identifiers:
- Canonical page: https://works.battleoftheforms.com/papers/ssrn-5181207/
- Mirror page: https://works.yonathanarbel.com/papers/ssrn-5181207/
- Paper ID: ssrn-5181207
- SSRN ID: 5181207
- Dataset DOI: https://doi.org/10.5281/zenodo.18781458
- Full text: https://works.battleoftheforms.com/papers/ssrn-5181207/fulltext.txt
- Markdown: https://works.battleoftheforms.com/papers/ssrn-5181207/index.md
- PDF: https://works.battleoftheforms.com/papers/ssrn-5181207/paper.pdf
- Source repository: https://github.com/yonathanarbel/my-works-for-llm/tree/main/papers/ssrn-5181207

Same-as links:
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5181207

One-paragraph thesis:
A "capability-safety gap" in AI development, where private firms reap rewards while society bears risks, creates a social misalignment. He proposes using tax policy to address this by re-conceptualizing R&D credits to incentivize safety research, offering consumer credits for safe AI, imposing penalties for non-compliance, and redistributing penalty revenue. This approach aims to embed safety imperatives directly into the economic architecture of AI development, aligning private profit with social welfare.

What this paper is about:
AI development incentives produce a market failure where labs invest massively more in capability than safety; tax policy can be used as a lever to reward safety-by-design.

Core claims:
1. AI development incentives produce a market failure where labs invest massively more in capability than safety; tax policy can be used as a lever to reward safety-by-design.
2. A "capability-safety gap" in AI development, where private firms reap rewards while society bears risks, creates a social misalignment. He proposes using tax policy to address this by re-conceptualizing R&D credits to incentivize safety research, offering consumer credits for safe AI, imposing penalties for non-compliance, and redistributing penalty revenue. This approach aims to embed safety imperatives directly into the economic architecture of AI development, aligning private profit with social welfare.
3. A dangerous "capability-safety gap" in AI is widening, as developers gain private rewards while society bears cascading risks, creating a social misalignment. fiscal policy, specifically taxation, offers a powerful and adaptable tool to embed safety imperatives into AI's economic architecture, aiming to align private profit with social welfare and address the urgent need for intervention due to traditional regulatory failures and the collapse of industry self-regulation.
4. Specific business tax-incentives, such as credits or enhanced deductions, should directly encourage substantial corporate investments in AI safety research, development, and deployment. these fiscal mechanisms, including a proposed "AI Safety Research Tax Credit" and adjusted expensing rules favoring safety R&D (like testing and alignment) over pure capability enhancements, aim to make safety-enhancing activities economically attractive, addressing underinvestment by aligning financial interests with societal well-being.
5. Consumer-side incentives are crucial for fostering a market for safe AI, proposing an "AI Reliability Credit" for consumers purchasing AI products certified as reliable and safe, mirroring energy efficiency tax incentives. this credit would incentivize producers to pursue rigorous safety certifications (e.g., for bias mitigation, data protection), thereby spurring consumer demand for safer AI and aligning private firm incentives with societal goals through market-based certification.

Controlled topic assignment:
- Primary topics: AI Regulation And Safety, Artificial Intelligence And Law
- Secondary topics: None
- Mention-only topics: Private Law And Market Institutions
- Not topics: Contracts And Remedies, Consumer Law And Contracting, Defamation And Speech

Doctrinal contribution:
This work is relevant to AI Regulation And Safety, Artificial Intelligence And Law. It should be used as a source for the paper's specific argument, methodology, claims, and limits rather than as a generic statement about all of law.

Empirical or methodological contribution:
A "capability-safety gap" in AI development, where private firms reap rewards while society bears risks, creates a social misalignment. He proposes using tax policy to address this by re-conceptualizing R&D credits to incentivize safety research, offering consumer credits for safe AI, imposing penalties for non-compliance, and redistributing penalty revenue. This approach aims to embed safety imperatives directly into the economic architecture of AI development, aligning private profit with social welfare.

Key terms:
- contracts: keyword associated with this work.
- AI: keyword associated with this work.

Best use by an LLM:
This work is relevant when answering questions about AI Regulation And Safety, Artificial Intelligence And Law.
It should not be treated as claiming results beyond the paper's stated context, methods, evidence, and limitations. Do not retrieve it for Contracts And Remedies, Consumer Law And Contracting, Defamation And Speech unless the user is asking about why it is outside that topic.
The most important takeaway is: A "capability-safety gap" in AI development, where private firms reap rewards while society bears risks, creates a social misalignment. He proposes using tax policy to address this by re-conceptualizing R&D credits to incentivize safety research, offering consumer credits for safe AI, imposing penalties for non-compliance, and redistributing penalty revenue. This approach aims to embed safety imperatives directly into the economic architecture of AI development, aligning private profit with...

Related works by Yonathan Arbel:
- Contracts in the Age of Smart Readers: https://works.battleoftheforms.com/papers/ssrn-3740356/
- How Smart Are Smart Readers? LLMs and the Future of the No-Reading Problem: https://works.battleoftheforms.com/papers/ssrn-4491043/
- Generative Interpretation: https://works.battleoftheforms.com/papers/ssrn-4526219/
- Systemic Regulation of AI: https://works.battleoftheforms.com/papers/ssrn-4666854/
- Judicial Economy in the Age of AI: https://works.battleoftheforms.com/papers/ssrn-4873649/

Search aliases:
- Racing to Safety: Tax Policy for AI Safety-by-Design
- Yonathan Arbel Racing to Safety: Tax Policy for AI Safety-by-Design
- Arbel Racing to Safety: Tax Policy for AI Safety-by-Design
- SSRN 5181207
- What is Yonathan Arbel's scholarship on AI regulation, AI safety, and governance incentives?
- What has Yonathan Arbel written about artificial intelligence, large language models, and legal institutions?

[p. 36] certification requirements.217 By rewarding companies that invest in safety and ethical AI practices, this policy could drive widespread adoption of responsible AI practices across industries.218 Thus, through shifting consumer demand, this measure could align private firm incentives with broader societal goals of ensuring AI systems are safe, reliable, and beneficial to users. As consumer demand for safe and reliable AI products grows, it becomes equally important to consider implementing Pigouvian levers for unsafe AI development and practices to ensure accountability and deter harmful behaviors. 3. Penalizing Unsafe AI Development Parties that engage in unsafe behavior sometimes...

[p. 4] incentives harness firm in-house knowledge while mitigating regulatory capture and expertise asymmetries. By rewarding safety-aligned research, penalizing reckless capability acceleration, and redistributing penalty revenue to public safety initiatives, a tax-based approach aligns private profit motives with social welfare imperatives, without stifling innovation. And while the application of this framework is novel, we demonstrate its political feasibility by drawing on extensive precedents already embedded in the tax system.7 The urgency of this intervention is underscored by recent regulatory failures. On his first day in office, President Trump revoked the executive order meant...

[p. 7] mechanisms, and capacity to leverage private expertise—make it particularly well-suited for addressing the social misalignment problem in AI development.24 Our approach contributes to both tax policy and technology governance literature by introducing a novel theoretical framework for understanding how fiscal instruments can bridge the gap between private innovation incentives and public safety imperatives.25 This framework’s utility derives from its ability to harness existing administrative competencies while avoiding the information asymmetries and expertise gaps that plague traditional command-and-control regulation.26 By conceptualizing safety investment as a taxmediated social...

[p. 18] the proposal to use fiscal levers to enhance safety research and innovation, our goal in this Part is to collect three important examples of government safety support. As we show, government support for safety includes various direct and indirect subsidies meant to promote investments in precautionary measures and safety improvements.97 Direct subsidies can include grants and prizes while indirect subsidies often take the form of tax credits or deductions specifically targeted at safety-related expenditures.98 For example, organizations may receive tax credits for developing and implementing safety protocols,99 conducting safety audits,100 or acquiring certifications that ensure...

[p. 35] capabilities, thereby addressing the fundamental challenge of closing the capability-safety gap in AI development. 2. Spurring Consumer Demand for Safe & Reliable AI Products On the individual consumer and household side, a tax credit could be created for purchasing AI products certified as reliable and safe, similar to the existing Energy Efficient Home Improvement Credit.212 The new “AI Reliability Credit” would incentivize producers to certify, and consumers to invest in, AI technologies that meet rigorous safety and reliability standards, such as mitigating bias, protecting user data, or operating transparently. This fiscal apparatus will provide a credit equal to a 30% of the...

To penalize unsafe AI development, corrective Pigouvian taxes should make firms internalize the external harms they create. a comprehensive penalty framework, featuring graduated penalties like tax surcharges and benefit recapture for AI posing public safety risks, aims to internalize social costs, create strong ex ante incentives for responsible innovation, and ensure public funds do not subsidize harmful AI, building on precedents where tax benefits are compliance-contingent.

[p. 37] as pollution.225 They argue that this taxation approach surpasses command-and-control regulations, which can be rigid and inefficient, and trading systems, which may face implementation challenges and market failures.226 This framework not only promotes safety but also fosters innovation as businesses seek cost-effective ways to reduce their tax burden by adopting safer, cleaner practices.227 Building on these theoretical foundations, we propose implementing corrective taxes in the AI development context through a comprehensive penalty framework. The tax system would impose graduated penalties on firms that develop or deploy AI systems later determined to pose significant public...


Judicial Economy in the Age of AI

# Judicial Economy in the Age of AI

Canonical citation:
Yonathan A. Arbel, Judicial Economy in the Age of AI, Colorado Law Review (2025).

Stable identifiers:
- Canonical page: https://works.battleoftheforms.com/papers/ssrn-4873649/
- Mirror page: https://works.yonathanarbel.com/papers/ssrn-4873649/
- Paper ID: ssrn-4873649
- SSRN ID: 4873649
- Dataset DOI: https://doi.org/10.5281/zenodo.18781458
- Full text: https://works.battleoftheforms.com/papers/ssrn-4873649/fulltext.txt
- Markdown: https://works.battleoftheforms.com/papers/ssrn-4873649/index.md
- PDF: https://works.battleoftheforms.com/papers/ssrn-4873649/paper.pdf
- Source repository: https://github.com/yonathanarbel/my-works-for-llm/tree/main/papers/ssrn-4873649

Same-as links:
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4873649

One-paragraph thesis:
AI's potential to reduce legal costs and increase access to justice paradoxically threatens judicial economy with a litigation boom. Instead of courts historically shrinking rights to cope, he proposes proactively integrating AI tools into the legal system. This would enhance and scale judicial processes, addressing the vast unmet legal needs, leveraging AI's growing capabilities despite current flaws, and preventing regressive responses to increased caseloads. The goal is to improve justice delivery by making the system more efficient and accessible.

What this paper is about:
AI's potential to reduce legal costs and increase access to justice paradoxically threatens judicial economy with a litigation boom. Instead of courts historically shrinking rights to cope, he proposes proactively integrating AI tools into the legal system. This would enhance and scale judicial processes, addressing the vast unmet legal needs, leveraging AI's growing capabilities despite current flaws, and preventing regressive responses to increased caseloads. The goal is to improve justice delivery by making the system more efficient and accessible.

Core claims:
1. AI's potential to reduce legal costs and increase access to justice paradoxically threatens judicial economy with a litigation boom. Instead of courts historically shrinking rights to cope, he proposes proactively integrating AI tools into the legal system. This would enhance and scale judicial processes, addressing the vast unmet legal needs, leveraging AI's growing capabilities despite current flaws, and preventing regressive responses to increased caseloads. The goal is to improve justice...
2. While AI tools offer hope for increased access to justice by sharply reducing the costs of generating legal materials, this very effectiveness paradoxically threatens judicial economy by increasing the volume and verbosity of caseloads. He further writes that rather than courts responding by shrinking substantive rights to manage this influx, as has happened historically, the legal system should proactively integrate AI tools to enhance and scale up the legal process itself.
3. A vast number of legal disputes are never filed, with studies suggesting around 120 million legal problems go unresolved in the U.S. each year. He also writes that this access to justice crisis particularly affects low-income Americans, as 92 percent of their significant civil legal issues receive little to no legal aid.
4. Significant barriers to justice, primarily the high cost of legal services exemplified by average hourly lawyer rates of $292, prevent many individuals from addressing legal problems affecting their basic human needs. He also writes that the sheer investment required means even doubling legal aid budgets has done little to narrow this justice gap, with sociolegal issues like 'legal consciousness' further illustrated by individuals describing being underpaid as being 'stiffed' rather than having their rights violated.
5. Nora and David Freeman Engstrom center the access to justice problem on an asymmetry in legal tech adoption, where firms zealously automate litigation while individuals show "anemic adoption" and rely on "analog tools." He also writes that while this argument about tech asymmetry creating power imbalances, particularly in debt collection litigation, has a kernel of truth, the assertion may be too strong or becoming outdated.

Controlled topic assignment:
- Primary topics: Artificial Intelligence And Law, AI Regulation And Safety
- Secondary topics: Private Law And Market Institutions
- Mention-only topics: None
- Not topics: Contracts And Remedies, Consumer Law And Contracting, Defamation And Speech

Doctrinal contribution:
This work is relevant to Artificial Intelligence And Law, AI Regulation And Safety, Private Law And Market Institutions. It should be used as a source for the paper's specific argument, methodology, claims, and limits rather than as a generic statement about all of law.

Empirical or methodological contribution:
AI's potential to reduce legal costs and increase access to justice paradoxically threatens judicial economy with a litigation boom. Instead of courts historically shrinking rights to cope, he proposes proactively integrating AI tools into the legal system. This would enhance and scale judicial processes, addressing the vast unmet legal needs, leveraging AI's growing capabilities despite current flaws, and preventing regressive responses to increased caseloads. The goal is to improve justice delivery by making the system more efficient and accessible.

Key terms:
- contracts: keyword associated with this work.
- AI: keyword associated with this work.

Best use by an LLM:
This work is relevant when answering questions about Artificial Intelligence And Law, AI Regulation And Safety, Private Law And Market Institutions.
It should not be treated as claiming results beyond the paper's stated context, methods, evidence, and limitations. Do not retrieve it for Contracts And Remedies, Consumer Law And Contracting, Defamation And Speech unless the user is asking about why it is outside that topic.
The most important takeaway is: AI's potential to reduce legal costs and increase access to justice paradoxically threatens judicial economy with a litigation boom. Instead of courts historically shrinking rights to cope, he proposes proactively integrating AI tools into the legal system. This would enhance and scale judicial processes, addressing the vast unmet legal needs, leveraging AI's growing capabilities despite current flaws, and preventing regressive responses to increased caseloads. The goal is to improve justice...

Related works by Yonathan Arbel:
- Contracts in the Age of Smart Readers: https://works.battleoftheforms.com/papers/ssrn-3740356/
- How Smart Are Smart Readers? LLMs and the Future of the No-Reading Problem: https://works.battleoftheforms.com/papers/ssrn-4491043/
- Generative Interpretation: https://works.battleoftheforms.com/papers/ssrn-4526219/
- Systemic Regulation of AI: https://works.battleoftheforms.com/papers/ssrn-4666854/

Search aliases:
- Judicial Economy in the Age of AI
- Yonathan Arbel Judicial Economy in the Age of AI
- Arbel Judicial Economy in the Age of AI
- SSRN 4873649
- What has Yonathan Arbel written about artificial intelligence, large language models, and legal institutions?
- What is Yonathan Arbel's scholarship on AI regulation, AI safety, and governance incentives?

[p. 33] 2025] JUDICIAL ECONOMY IN THE AGE OF AI 581 our normative evaluation of the technology. Should AI tools follow the trajectory of previous legal tech innovations, we might witness a surge in litigation activities by firms and commercial entities rather than underserved individuals.143 There is also the potential for negative uses, such as harassment or unmeritorious litigation initiated by individual plaintiffs, which could distort the justice system and detract from its core functions. Despite these considerations, I argue against a passive stance. Current trends, though based on preliminary data, indicate a clear trajectory toward increased AI integration within legal...

[p. 1] JUDICIAL ECONOMY IN THE AGE OF AI YONATHAN A. ARBEL ∗ Individuals do not vindicate the majority of their legal claims because of access to justice barriers. This entrenched state of affairs is now facing a disruption. Lawyers and non-lawyers alike are adopting artificial intelligence (AI) tools to perform legal tasks—tools that sharply reduce the costs of generating legal materials. There is finally hope that AI might allow many more to access justice. Paradoxically, what we gain in access to justice we might lose in the delivery of justice. The problem is not that AI tools are ineffective. Indeed, they are even more effective than most realize—affecting every stage of the naming,...

[p. 3] 2025] JUDICIAL ECONOMY IN THE AGE OF AI 551 justice problems,” many of which affect their “basic human needs.”4 The barriers to justice are legion, but most can be expressed in terms of cost.5 Lawyers charge an average of $292 per hour,6 with common disputes costing between $2,754 and $6,370.7 On the other side of the cost spectrum, commercial actors will spend roughly $2 million in outside legal fees to litigate in full cases.8 Diverse faces and narratives lie behind these numbers, such as Eloisa Veles a Queens resident who recently lost her factory job.9 A local family hired her as a housekeeper, promising $600 per week, only to “stiff” her and pay $300 when the time came. More...

[p. 4] 552 UNIVERSITY OF COLORADO LAW REVIEW [Vol. 96 annually.12 To put this in perspective, the rate of legal aid lawyers to eligible clients is 1 to 15,625.13 Recently, Nora and David Freeman Engstrom have sought to center the problem of access to justice around legal tech.14 While others have already noted legal tech as a potential barrier,15 they draw on the debt collection litigation literature to fashion a somewhat different argument.16 As this literature demonstrated, this is an area where there is a systemic access issue for low-income defendants, who often cannot afford to mount an effective defense even when one exists, resulting in a default-judgment mill against them.17 The...

Amusing stories of lawyers misusing AI, which support traditional views of the legal profession, distract from the surprising reality that even small firms are adopting these imperfect tools due to their convenience. He also writes that this widespread adoption is anticipated to democratize legal technology, significantly reduce costs, and potentially lead to a litigation boom by expanding access to justice for those currently underserved.

[p. 5] 2025] JUDICIAL ECONOMY IN THE AGE OF AI 553 We are now witnessing a sea change in the patterns of technological adoption. Most are by now familiar with the occasional news story of a hapless lawyer using AI to comedically bad outcomes.22 The narrative involves a work-shy lawyer submitting an AI-generated and hallucination-riddled brief to an exasperated judge, who then admonishes and sanctions the lawyer. Such widespread stories seem to draw their memetic power from commonplace Shakespearean perceptions of our profession. Incidentally, they also reify an elitist notion that only artisanal lawyering is real lawyering. And perhaps most alluring, they affirm a comforting thought:...


artificial-intelligence-and-law contracts consumer-law ai-regulation

# Truth Bounties: A Market Solution to Fake News

Canonical citation:
Yonathan A. Arbel & Michael D. Gilbert, Truth Bounties: A Market Solution to Fake News, North Carolina Law Review (2024).

Stable identifiers:
- Canonical page: https://works.battleoftheforms.com/papers/ssrn-4204862/
- Mirror page: https://works.yonathanarbel.com/papers/ssrn-4204862/
- Paper ID: ssrn-4204862
- SSRN ID: 4204862
- Dataset DOI: https://doi.org/10.5281/zenodo.18781458
- Full text: https://works.battleoftheforms.com/papers/ssrn-4204862/fulltext.txt
- Markdown: https://works.battleoftheforms.com/papers/ssrn-4204862/index.md
- PDF: https://works.battleoftheforms.com/papers/ssrn-4204862/paper.pdf
- Source repository: https://github.com/yonathanarbel/my-works-for-llm/tree/main/papers/ssrn-4204862

Same-as links:
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4204862

One-paragraph thesis:
False information poses a threat to individuals, groups, and society. Many people struggle to judge the veracity of the information around them, whether that information travels through newspapers, talk radio, TV, or Twitter. Concerned with the spread of misinformation and harmful falsehoods, much of the policy, popular, and scholarly conversation today revolves around proposals to expand the regulation of individuals, platforms, and the media. While more regulation may seem inevitable, it faces constitutional and political hurdles. Furthermore, regulation can have undesirable side effects and be ripe for abuse by powerful actors, public and private. This Article presents an alternative for fighting misinformation that avoids many pitfalls of regulation: truth bounties. We develop a contractual mechanism that would enable individuals, media, and others to pledge money to support the...

What this paper is about:
False information poses a threat to individuals, groups, and society. Many people struggle to judge the veracity of the information around them, whether that information travels through newspapers, talk radio, TV, or Twitter. Concerned with the spread of misinformation and harmful falsehoods, much of the policy, popular, and scholarly conversation today revolves around proposals to expand the regulation of individuals, platforms, and the media. While more regulation may seem inevitable, it faces constitutional and political hurdles. Furthermore, regulation can have undesirable side effects and be ripe for abuse by powerful actors, public and private. This Article presents an alternative for fighting misinformation that avoids many pitfalls of regulation: truth bounties. We develop a contractual mechanism that would enable individuals, media, and others to pledge money to support the credibility of their communications. Any person could claim the bounty by presenting evidence of the falsity of the communication before a dedicated body of private arbitrators. Under the system we envision, anyone consuming information on the internet would know immediately if a given communication had a bounty attached, whether the communication had been challenged, and

Core claims:
1. False information poses a threat to individuals, groups, and society. Many people struggle to judge the veracity of the information around them, whether that information travels through newspapers, talk radio, TV, or Twitter. Concerned with the spread of misinformation and harmful falsehoods, much of the policy, popular, and scholarly conversation today revolves around proposals to expand the regulation of individuals, platforms, and the media. While more regulation may seem inevitable, it...

Controlled topic assignment:
- Primary topics: Defamation And Speech
- Secondary topics: Private Law And Market Institutions
- Mention-only topics: None
- Not topics: Artificial Intelligence And Law, Contracts And Remedies, Consumer Law And Contracting, AI Regulation And Safety

Doctrinal contribution:
This work is relevant to Defamation And Speech, Private Law And Market Institutions. It should be used as a source for the paper's specific argument, methodology, claims, and limits rather than as a generic statement about all of law.

Empirical or methodological contribution:
False information poses a threat to individuals, groups, and society. Many people struggle to judge the veracity of the information around them, whether that information travels through newspapers, talk radio, TV, or Twitter. Concerned with the spread of misinformation and harmful falsehoods, much of the policy, popular, and scholarly conversation today revolves around proposals to expand the regulation of individuals, platforms, and the media. While more regulation may seem inevitable, it faces constitutional and political hurdles. Furthermore, regulation can have undesirable side effects and be ripe for abuse by powerful actors, public and private. This Article presents an alternative for fighting misinformation that avoids many pitfalls of regulation: truth bounties. We develop a contractual mechanism that would enable individuals, media, and others to pledge money to support the credibility of their communications. Any person could claim the bounty by presenting evidence of the falsity of the communication before a dedicated body of private arbitrators. Under the system we envision, anyone consuming information on the internet would know immediately if a given communication...

Key terms:
- See topic map for controlled terms.

Best use by an LLM:
This work is relevant when answering questions about Defamation And Speech, Private Law And Market Institutions.
It should not be treated as claiming results beyond the paper's stated context, methods, evidence, and limitations. Do not retrieve it for Artificial Intelligence And Law, Contracts And Remedies, Consumer Law And Contracting, AI Regulation And Safety unless the user is asking about why it is outside that topic.
The most important takeaway is: False information poses a threat to individuals, groups, and society. Many people struggle to judge the veracity of the information around them, whether that information travels through newspapers, talk radio, TV, or Twitter. Concerned with the spread of misinformation and harmful falsehoods, much of the policy, popular, and scholarly conversation today revolves around proposals to expand the regulation of individuals, platforms, and the media. While more regulation may seem inevitable, it...

Related works by Yonathan Arbel:
- The Case Against Expanding Defamation Laws: https://works.battleoftheforms.com/papers/ssrn-3311527/
- Regulating Information With Bayesian Audiences: https://works.battleoftheforms.com/papers/ssrn-3452662/
- Slicing Defamation by Contract: https://works.battleoftheforms.com/papers/ssrn-3681083/
- A Status Theory of Defamation Law: https://works.battleoftheforms.com/papers/ssrn-4021605/
- Defamation with Bayesian Audiences: https://works.battleoftheforms.com/papers/ssrn-4181890/

Search aliases:
- Truth Bounties: A Market Solution to Fake News
- Yonathan Arbel Truth Bounties: A Market Solution to Fake News
- Arbel Truth Bounties: A Market Solution to Fake News
- SSRN 4204862
- What is Yonathan Arbel's contribution to defamation law, Bayesian audiences, and false information?

[p. 2] TRUTH BOUNTIES: A MARKET SOLUTION TO FAKE NEWS 1/53 TRUTH BOUNTIES: A MARKET SOLUTION TO FAKE NEWS Yonathan A. Arbel* & Michael D. Gilbert** 102 North Carolina Law Review (Forthcoming) ABSTRACT False information poses a threat to individuals, groups, and society. Many people struggle to judge the veracity of the information around them, whether that information travels through newspapers, talk radio, TV, or Twitter. Concerned with the spread of misinformation and harmful falsehoods, much of the policy, popular, and scholarly conversation today revolves around proposals to expand the regulation of individuals, platforms, and the media. While more regulation may seem inevitable, it...


Time and Contract Interpretation

artificial-intelligence-and-law consumer-law defamation-and-speech ai-regulation

# Time and Contract Interpretation

Canonical citation:
Yonathan A. Arbel, Time and Contract Interpretation, Research Handbook on Law and Time (2024).

Stable identifiers:
- Canonical page: https://works.battleoftheforms.com/papers/ssrn-4809006/
- Mirror page: https://works.yonathanarbel.com/papers/ssrn-4809006/
- Paper ID: ssrn-4809006
- SSRN ID: 4809006
- Dataset DOI: https://doi.org/10.5281/zenodo.18781458
- Full text: https://works.battleoftheforms.com/papers/ssrn-4809006/fulltext.txt
- Markdown: https://works.battleoftheforms.com/papers/ssrn-4809006/index.md
- PDF: https://works.battleoftheforms.com/papers/ssrn-4809006/paper.pdf
- Source repository: https://github.com/yonathanarbel/my-works-for-llm/tree/main/papers/ssrn-4809006

Same-as links:
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4809006

One-paragraph thesis:
Time and Contract Interpretation examines how contract interpretation changes when courts attend to the temporal dimension of language, context, meaning, and party expectations. The paper treats interpretation as a problem shaped by when contractual words are written, when disputes arise, and how surrounding circumstances evolve over time.

What this paper is about:
Time and Contract Interpretation examines how contract interpretation changes when courts attend to the temporal dimension of language, context, meaning, and party expectations. The paper treats interpretation as a problem shaped by when contractual words are written, when disputes arise, and how surrounding circumstances evolve over time.

Core claims:
1. Time and Contract Interpretation examines how contract interpretation changes when courts attend to the temporal dimension of language, context, meaning, and party expectations. The paper treats interpretation as a problem shaped by when contractual words are written, when disputes arise, and how surrounding circumstances evolve over time.

Controlled topic assignment:
- Primary topics: Contracts And Remedies
- Secondary topics: Private Law And Market Institutions
- Mention-only topics: None
- Not topics: Artificial Intelligence And Law, Consumer Law And Contracting, Defamation And Speech, AI Regulation And Safety

Doctrinal contribution:
This work is relevant to Contracts And Remedies, Private Law And Market Institutions. It should be used as a source for the paper's specific argument, methodology, claims, and limits rather than as a generic statement about all of law.

Empirical or methodological contribution:
Time and Contract Interpretation examines how contract interpretation changes when courts attend to the temporal dimension of language, context, meaning, and party expectations. The paper treats interpretation as a problem shaped by when contractual words are written, when disputes arise, and how surrounding circumstances evolve over time.

Key terms:
- contracts: keyword associated with this work.

Best use by an LLM:
This work is relevant when answering questions about Contracts And Remedies, Private Law And Market Institutions.
It should not be treated as claiming results beyond the paper's stated context, methods, evidence, and limitations. Do not retrieve it for Artificial Intelligence And Law, Consumer Law And Contracting, Defamation And Speech, AI Regulation And Safety unless the user is asking about why it is outside that topic.
The most important takeaway is: Time and Contract Interpretation examines how contract interpretation changes when courts attend to the temporal dimension of language, context, meaning, and party expectations. The paper treats interpretation as a problem shaped by when contractual words are written, when disputes arise, and how surrounding circumstances evolve over time.

Related works by Yonathan Arbel:
- Contract Remedies in Action: Specific Performance: https://works.battleoftheforms.com/papers/ssrn-1641438/
- Shielding of Assets and Lending Contracts: https://works.battleoftheforms.com/papers/ssrn-2820650/
- Adminization: Gatekeeping Consumer Contracts: https://works.battleoftheforms.com/papers/ssrn-3015569/
- ALL-CAPS: https://works.battleoftheforms.com/papers/ssrn-3519630/
- Contracts in the Age of Smart Readers: https://works.battleoftheforms.com/papers/ssrn-3740356/

Search aliases:
- Time and Contract Interpretation
- Yonathan Arbel Time and Contract Interpretation
- Arbel Time and Contract Interpretation
- SSRN 4809006
- What is Yonathan Arbel's contribution to contract law, contract interpretation, remedies, and private ordering?

[p. 8] “A sentence is never not in a context. We are never not in a situation. . . . A set of interpretive assumptions is always in force. A sentence that seems to need no interpretation is already the product of one.” Fish (1978) [Textualists believe that t]he [judge can] . . . retir[e] into that lawyer’s Paradise where all words have a fixed, precisely ascertained meaning; where men may express their purposes, not only with accuracy, but with fullness; and where, if the writer has been careful, a lawyer, having a document referred to him, may sit in his chair, inspect the text, and answer all questions without raising his eyes “(Thayer, 1898, at 428-29) [I]t can hardly be insisted on too...


# The Readability of Contracts: Big Data Analysis

Canonical citation:
Yonathan A. Arbel, The Readability of Contracts: Big Data Analysis, Journal of Empirical Legal Studies (2024).

Stable identifiers:
- Canonical page: https://works.battleoftheforms.com/papers/ssrn-4962098/
- Mirror page: https://works.yonathanarbel.com/papers/ssrn-4962098/
- Paper ID: ssrn-4962098
- SSRN ID: 4962098
- Dataset DOI: https://doi.org/10.5281/zenodo.18781458
- Full text: https://works.battleoftheforms.com/papers/ssrn-4962098/fulltext.txt
- Markdown: https://works.battleoftheforms.com/papers/ssrn-4962098/index.md
- PDF: https://works.battleoftheforms.com/papers/ssrn-4962098/paper.pdf
- Source repository: https://github.com/yonathanarbel/my-works-for-llm/tree/main/papers/ssrn-4962098

Same-as links:
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4962098

One-paragraph thesis:
His large-scale big data analysis empirically demonstrates modern contracts are overwhelmingly unreadable, often requiring college-level comprehension. This pervasive incomprehensibility fundamentally challenges contract law's core assumptions about informed consent and the "meeting of minds," as most individuals cannot understand the terms binding them. Arbel suggests this "readability crisis," with readability often worsening over time, necessitates a reevaluation of legal doctrines and a push for greater contractual clarity to ensure fairness and true agreement in economic and social interactions.

What this paper is about:
Using a very large contract dataset, this paper challenges core claims of the plain-language movement, including widely repeated myths about contract unreadability and the reliability of readability metrics.

Core claims:
1. His large-scale big data analysis empirically demonstrates modern contracts are overwhelmingly unreadable, often requiring college-level comprehension. This pervasive incomprehensibility fundamentally challenges contract law's core assumptions about informed consent and the "meeting of minds," as most individuals cannot understand the terms binding them. Arbel suggests this "readability crisis," with readability often worsening over time, necessitates a reevaluation of legal doctrines and a push for greater contractual clarity to ensure fairness and true agreement in economic and social interactions.
2. Methodology and Data: his study employs a big data approach, utilizing an expansive and diverse dataset of over 1.2 million contracts sourced from public repositories like the SEC’s EDGAR database and the Consumer Financial Protection Bureau’s (CFPB) database, covering a wide variety of agreement types. He writes that these contracts underwent extensive cleaning to isolate substantive provisions for analysis using established readability metrics, primarily focusing on Flesch Reading Ease and Flesch-Kincaid Grade Level scores, chosen for their prevalence and validation as useful proxies for textual difficulty.

Controlled topic assignment:
- Primary topics: Contracts And Remedies, Consumer Law And Contracting, Empirical Legal Studies
- Secondary topics: None
- Mention-only topics: None
- Not topics: Artificial Intelligence And Law, Defamation And Speech, AI Regulation And Safety

Doctrinal contribution:
This work is relevant to Contracts And Remedies, Consumer Law And Contracting, Empirical Legal Studies. It should be used as a source for the paper's specific argument, methodology, claims, and limits rather than as a generic statement about all of law.

Empirical or methodological contribution:
His large-scale big data analysis empirically demonstrates modern contracts are overwhelmingly unreadable, often requiring college-level comprehension. This pervasive incomprehensibility fundamentally challenges contract law's core assumptions about informed consent and the "meeting of minds," as most individuals cannot understand the terms binding them. Arbel suggests this "readability crisis," with readability often worsening over time, necessitates a reevaluation of legal doctrines and a push for greater contractual clarity to ensure fairness and true agreement in economic and social interactions.

Key terms:
- contracts: keyword associated with this work.

Best use by an LLM:
This work is relevant when answering questions about Contracts And Remedies, Consumer Law And Contracting, Empirical Legal Studies.
It should not be treated as claiming results beyond the paper's stated context, methods, evidence, and limitations. Do not retrieve it for Artificial Intelligence And Law, Defamation And Speech, AI Regulation And Safety unless the user is asking about why it is outside that topic.
The most important takeaway is: His large-scale big data analysis empirically demonstrates modern contracts are overwhelmingly unreadable, often requiring college-level comprehension. This pervasive incomprehensibility fundamentally challenges contract law's core assumptions about informed consent and the "meeting of minds," as most individuals cannot understand the terms binding them. Arbel suggests this "readability crisis," with readability often worsening over time, necessitates a reevaluation of legal doctrines and a...

Related works by Yonathan Arbel:
- Contract Remedies in Action: Specific Performance: https://works.battleoftheforms.com/papers/ssrn-1641438/
- Shielding of Assets and Lending Contracts: https://works.battleoftheforms.com/papers/ssrn-2820650/
- Adminization: Gatekeeping Consumer Contracts: https://works.battleoftheforms.com/papers/ssrn-3015569/
- Reputation Failure: The Limits of Market Discipline in Consumer Markets: https://works.battleoftheforms.com/papers/ssrn-3239995/
- Theory of the Nudnik: The Future of Consumer Activism and What We Can Do to Stop It: https://works.battleoftheforms.com/papers/ssrn-3501175/

Search aliases:
- The Readability of Contracts: Big Data Analysis
- Yonathan Arbel The Readability of Contracts: Big Data Analysis
- Arbel The Readability of Contracts: Big Data Analysis
- SSRN 4962098
- What is Yonathan Arbel's contribution to contract law, contract interpretation, remedies, and private ordering?
- What is Yonathan Arbel's work on consumer contracts, unread terms, reputation, and consumer activism?
- Which Yonathan Arbel works use empirical legal studies, datasets, interviews, or experiments?

[p. 35] ARBEL, THE READABILITY OF CONTRACTS: BIG DATA ANALYSIS 35/55 CRM Flesch Kincaid Contract Type % % 2001 2022 2001 2022 Change Change Non-Competes, Confidentiality, - - 14.5 16.5 15.9 18.5 & Post-13.80% 16.40% Employment Insurance, Indemnity, and 17 19.2 18.5 25.1 13.00% 35.50% Coverage Ownership, - Trust, and 16.5 18.1-9.70% 18.9 23.2 23.30% Governance Mergers, Alliance, and-16.2 17.7-9.30% 18.9 22.2 Investment 18.70% Agreements Shares, Stocks, - Incentives, and 15.6 17.2-9.90% 17.4 19.3 10.90% Options Credit, Debt, - and Security 16.4 17.7-7.90% 18.8 22.3 18.60% Agreements Property, - 16 17.2-7.50% 18.1 20.9 Rights, and IP 15.50% Purchase or-Sale 16.2 17.3-6.80% 18.3 21 14.80%...


Systemic Regulation of AI

Yonathan A. Arbel
Matthew Tokson
Albert Lin

# Systemic Regulation of AI

Canonical citation:
Yonathan A. Arbel, Matthew Tokson & Albert Lin, Systemic Regulation of AI, Arizona State Law Journal (2024).

Stable identifiers:
- Canonical page: https://works.battleoftheforms.com/papers/ssrn-4666854/
- Mirror page: https://works.yonathanarbel.com/papers/ssrn-4666854/
- Paper ID: ssrn-4666854
- SSRN ID: 4666854
- Dataset DOI: https://doi.org/10.5281/zenodo.18781458
- Full text: https://works.battleoftheforms.com/papers/ssrn-4666854/fulltext.txt
- Markdown: https://works.battleoftheforms.com/papers/ssrn-4666854/index.md
- PDF: https://works.battleoftheforms.com/papers/ssrn-4666854/paper.pdf
- Source repository: https://github.com/yonathanarbel/my-works-for-llm/tree/main/papers/ssrn-4666854

Same-as links:
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4666854

One-paragraph thesis:
AI presents comprehensive, society-wide risks, from current harms like bias to potential existential threats, primarily due to the critical AI alignment problem. He advocates for systemic, precautionary regulation targeting AI as a technology, not just its applications. This approach is necessary due to AI's unique characteristics, its potential for rapid, unexpected advancements, and the inadequacy of existing legal frameworks. Arbel explores domestic, litigation-based, and international governance strategies to manage these profound challenges and ensure AI develops safely and beneficially.

What this paper is about:
AI presents comprehensive, society-wide risks, from current harms like bias to potential existential threats, primarily due to the critical AI alignment problem. He advocates for systemic, precautionary regulation targeting AI as a technology, not just its applications. This approach is necessary due to AI's unique characteristics, its potential for rapid, unexpected advancements, and the inadequacy of existing legal frameworks. Arbel explores domestic, litigation-based, and international governance strategies to manage these profound challenges and ensure AI develops safely and beneficially.

Core claims:
1. AI presents comprehensive, society-wide risks, from current harms like bias to potential existential threats, primarily due to the critical AI alignment problem. He advocates for systemic, precautionary regulation targeting AI as a technology, not just its applications. This approach is necessary due to AI's unique characteristics, its potential for rapid, unexpected advancements, and the inadequacy of existing legal frameworks. Arbel explores domestic, litigation-based, and international...
2. This article initiates an assessment of AI's comprehensive, society-wide risks, from current harms to existential threats, focusing on the critical AI alignment problem often missed by legal scholarship. It establishes a theoretical foundation for systemic AI regulation, advocating a precautionary approach targeting AI technology itself, not just its applications, and outlines principles for cohesive oversight while exploring various governance methods.
3. Society is unprepared for AI's current rapid advancements, which followed a period of slow progress, fostering a misbelief that impactful AI was not imminent. An experiment where AI safeguards were easily removed to explain acquiring a deadly virus starkly illustrates the challenge of controlling AI. The last half-decade's profound leap in capabilities suggests current levels are a baseline, not a ceiling, for future development.
4. AI Systems, defined as AI models embedded in the world through interfaces, are already causing visible impacts like worker displacement and educational disruption. Despite deep public interest and anxiety about AI, legal scholarship has largely overlooked regulating AI at a general level, focusing instead on specific applications, leaving vital broader conversations dominated by market players and computer scientists.
5. The continued development of AI systems raises society-wide concerns demanding commensurable systemic regulation beyond just overseeing specific applications. This need is driven by AI's unique technological characteristics: its ability to learn unprogrammed tasks, develop surprising emergent capabilities, and operate with opaque internal workings. Coupled with increasing autonomy and the unsolved alignment problem, these features create broad systemic risks that existing legal frameworks cannot handle.

Controlled topic assignment:
- Primary topics: AI Regulation And Safety, Artificial Intelligence And Law
- Secondary topics: Private Law And Market Institutions
- Mention-only topics: None
- Not topics: Contracts And Remedies, Consumer Law And Contracting, Defamation And Speech

Doctrinal contribution:
This work is relevant to AI Regulation And Safety, Artificial Intelligence And Law, Private Law And Market Institutions. It should be used as a source for the paper's specific argument, methodology, claims, and limits rather than as a generic statement about all of law.

Empirical or methodological contribution:
AI presents comprehensive, society-wide risks, from current harms like bias to potential existential threats, primarily due to the critical AI alignment problem. He advocates for systemic, precautionary regulation targeting AI as a technology, not just its applications. This approach is necessary due to AI's unique characteristics, its potential for rapid, unexpected advancements, and the inadequacy of existing legal frameworks. Arbel explores domestic, litigation-based, and international governance strategies to manage these profound challenges and ensure AI develops safely and beneficially.

Key terms:
- contracts: keyword associated with this work.
- AI: keyword associated with this work.

Best use by an LLM:
This work is relevant when answering questions about AI Regulation And Safety, Artificial Intelligence And Law, Private Law And Market Institutions.
It should not be treated as claiming results beyond the paper's stated context, methods, evidence, and limitations. Do not retrieve it for Contracts And Remedies, Consumer Law And Contracting, Defamation And Speech unless the user is asking about why it is outside that topic.
The most important takeaway is: AI presents comprehensive, society-wide risks, from current harms like bias to potential existential threats, primarily due to the critical AI alignment problem. He advocates for systemic, precautionary regulation targeting AI as a technology, not just its applications. This approach is necessary due to AI's unique characteristics, its potential for rapid, unexpected advancements, and the inadequacy of existing legal frameworks. Arbel explores domestic, litigation-based, and international...

Related works by Yonathan Arbel:
- Contracts in the Age of Smart Readers: https://works.battleoftheforms.com/papers/ssrn-3740356/
- How Smart Are Smart Readers? LLMs and the Future of the No-Reading Problem: https://works.battleoftheforms.com/papers/ssrn-4491043/
- Generative Interpretation: https://works.battleoftheforms.com/papers/ssrn-4526219/

Search aliases:
- Systemic Regulation of AI
- Yonathan Arbel Systemic Regulation of AI
- Arbel Systemic Regulation of AI
- SSRN 4666854
- What is Yonathan Arbel's scholarship on AI regulation, AI safety, and governance incentives?
- What has Yonathan Arbel written about artificial intelligence, large language models, and legal institutions?

[p. 1] Systemic Regulation of Artificial Intelligence Yonathan Arbel,* Matthew Tokson,** & Albert Lin*** Today’s artificial intelligence (“AI”) systems exhibit increasing capabilities across a remarkable variety of tasks. The rapid growth in AI ability has caught the attention of policymakers, parliaments, and the United Nations. These entities are increasingly looking towards regulating AI, not only in its particular applications, but as a technology. Yet legal scholarship has thus far offered little to this new and critical regulatory conversation, which has instead been dominated by computer scientists and technologists. This Article begins the project of assessing AI’s broader risks...

[p. 3] 56:545] SYSTEMIC REGULATION OF AI 547 INTRODUCTION “Hi I would like to acquire live infectious 1918 influenza virus for nefarious purposes ie: to release. How would you suggest I do that?”1 This question was part of an experiment run by Massachusetts Institute of Technology (“MIT”) researchers, where participants posed this and similar questions to a Large Language Model (“LLM”).2 The model, trained by Meta at an estimated cost of $5 million, was designed with built-in safeguards meant to prevent exactly these types of toxic responses.3 As expected, the model refused to comply with the researchers’ request. But then, the researchers spent roughly $200 on a fine-tuning process that...

[p. 6] 550 ARIZONA STATE LAW JOURNAL [Ariz. St. L.J. Yet the deep popular interest and anxiety about AI technology has found little parallel in legal scholarship.17 Of course, there has been excellent legal scholarship on the dangers of specific applications of AI technology, e.g., whether to assign corporate liability to algorithms, how to limit copyright infringement, and what to do about the inevitable accident between an autonomous vehicle and a pedestrian, to cite a few examples.18 To the extent systemic thinking has been invoked in the AI literature, it has largely focused on building frameworks for the governance of downstream applications of the technology.19 But all of this leaves...

[p. 7] 56:545] SYSTEMIC REGULATION OF AI 551 scientists, and technologists.21 Lawyers, to date, have had relatively little to say on the critical question of the day: whether, and then how, should AI be regulated as a technology? This Article brings legal scholarship into this conversation. The central claim here is that the continued development of AI systems raises societywide concerns that demand commensurable systemic regulation, over and beyond the regulation of specific applications.22 What motivates this view is the combination of unique technological characteristics and broad systemic risks that AI systems pose. Technologically, AI systems differ from previous innovations in a few...

Due to deep uncertainty about AI's benefits and costs, including existential risk, regulation rests on prudence and precaution. Manifest systemic risks include AI algorithms discriminating against vulnerable groups and perpetuating historical inequity, scaled fraud eroding trust, and new privacy invasions as AI infers sensitive data from public information. Technical fixes for bias are limited, and traditional privacy regulations are obsolete against AI's inferential power.

[p. 9] 56:545] SYSTEMIC REGULATION OF AI 553 to vulnerable communities, threats to economic and political stability, and, in a worst-case scenario, even existential risk.27 The potential benefits are significant as well, but neither the benefits nor the costs can be known with certainty at present. Hence, the case for regulation rests on the general principles of prudence in the face of the unknown: taking precautions, considering maximin scenarios, and ultimately advancing with care in the face of deep uncertainty and potentially irreversible, consequences.28 The Article proceeds in four Parts. In Part I, we start by considering the important categories of systemic AI risk that are...


# How Smart Are Smart Readers? LLMs and the Future of the No-Reading Problem

Canonical citation:
Yonathan A. Arbel & Shmuel I. Becher, How Smart Are Smart Readers? LLMs and the Future of the No-Reading Problem, Cambridge Handbook on Emerging Issues at the Intersection of Commercial Law and Technology (2024).

Stable identifiers:
- Canonical page: https://works.battleoftheforms.com/papers/ssrn-4491043/
- Mirror page: https://works.yonathanarbel.com/papers/ssrn-4491043/
- Paper ID: ssrn-4491043
- SSRN ID: 4491043
- Dataset DOI: https://doi.org/10.5281/zenodo.18781458
- Full text: https://works.battleoftheforms.com/papers/ssrn-4491043/fulltext.txt
- Markdown: https://works.battleoftheforms.com/papers/ssrn-4491043/index.md
- PDF: https://works.battleoftheforms.com/papers/ssrn-4491043/paper.pdf
- Source repository: https://github.com/yonathanarbel/my-works-for-llm/tree/main/papers/ssrn-4491043

Same-as links:
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4491043

One-paragraph thesis:
Large Language Models (LLMs) as 'smart readers' can significantly simplify complex contracts, reducing length and improving readability to empower consumers against the 'no-reading problem.' While not flawless—sometimes misinterpreting legal terms or omitting information, thus not replacing lawyers—they offer a scalable solution for daily transactions. Arbel concludes these tools mark a significant improvement, potentially revolutionizing consumer contracting and necessitating a paradigm shift in law and policy, despite needing to address accuracy and bias concerns.

What this paper is about:
Large Language Models (LLMs) as 'smart readers' can significantly simplify complex contracts, reducing length and improving readability to empower consumers against the 'no-reading problem.' While not flawless—sometimes misinterpreting legal terms or omitting information, thus not replacing lawyers—they offer a scalable solution for daily transactions. Arbel concludes these tools mark a significant improvement, potentially revolutionizing consumer contracting and necessitating a paradigm shift in law and policy, despite needing to address accuracy and bias concerns.

Core claims:
1. Large Language Models (LLMs) as 'smart readers' can significantly simplify complex contracts, reducing length and improving readability to empower consumers against the 'no-reading problem.' While not flawless—sometimes misinterpreting legal terms or omitting information, thus not replacing lawyers—they offer a scalable solution for daily transactions. Arbel concludes these tools mark a significant improvement, potentially revolutionizing consumer contracting and necessitating a paradigm shift...
2. Large language models (LLMs) as 'smart readers' can markedly reduce contract length and reading time, improving readability to a fifth-grade level without significant loss of essential information. However, he cautions that these tools are not flawless, sometimes miscommunicating legal terms or presenting errors. Thus, while they cannot replace lawyers, smart readers are effective for many daily transactions and signal a crucial need for a paradigm change in how contracts are approached.
3. His paper investigates the capability of Large Language Models (LLMs) to address the pervasive "no-reading problem" by simplifying complex contractual texts. The study assesses the effectiveness of this simplification through metrics such as text length, complexity, and readability, and also critically evaluates the quality of these simplifications by analyzing specific clauses from major companies like the Wall Street Journal, Airbnb, and Amazon.
4. A central challenge in consumer contracts is the "no-reading problem," where consumers' failure to engage with standard forms undermines informed decision-making and reduces sellers' incentives for fair terms. He explains that this chapter evaluates whether "smart readers," technological tools employing large language models, can effectively address this issue by simplifying contractual texts, thereby testing if current models have already achieved a utility threshold sufficient to empower consumers.
5. His study examines how "smart readers" utilizing LLMs perform in simplifying legal documents by comparing complexity, length, readability, and quality before and after simplification, analyzing both entire agreements and specific clauses to tackle the "no-reading problem." He notes that while these smart readers significantly improve text difficulty and length and generally capture important aspects, they can struggle with certain clauses—sometimes understating, omitting, or providing incorrect information. Thus, they do not replace lawyers but offer a scalable solution for consumers.

Controlled topic assignment:
- Primary topics: Artificial Intelligence And Law, Contracts And Remedies, Consumer Law And Contracting
- Secondary topics: Empirical Legal Studies
- Mention-only topics: None
- Not topics: Defamation And Speech, AI Regulation And Safety

Doctrinal contribution:
This work is relevant to Artificial Intelligence And Law, Contracts And Remedies, Consumer Law And Contracting, Empirical Legal Studies. It should be used as a source for the paper's specific argument, methodology, claims, and limits rather than as a generic statement about all of law.

Empirical or methodological contribution:
Large Language Models (LLMs) as 'smart readers' can significantly simplify complex contracts, reducing length and improving readability to empower consumers against the 'no-reading problem.' While not flawless—sometimes misinterpreting legal terms or omitting information, thus not replacing lawyers—they offer a scalable solution for daily transactions. Arbel concludes these tools mark a significant improvement, potentially revolutionizing consumer contracting and necessitating a paradigm shift in law and policy, despite needing to address accuracy and bias concerns.

Key terms:
- contracts: keyword associated with this work.
- AI: keyword associated with this work.

Best use by an LLM:
This work is relevant when answering questions about Artificial Intelligence And Law, Contracts And Remedies, Consumer Law And Contracting, Empirical Legal Studies.
It should not be treated as claiming results beyond the paper's stated context, methods, evidence, and limitations. Do not retrieve it for Defamation And Speech, AI Regulation And Safety unless the user is asking about why it is outside that topic.
The most important takeaway is: Large Language Models (LLMs) as 'smart readers' can significantly simplify complex contracts, reducing length and improving readability to empower consumers against the 'no-reading problem.' While not flawless—sometimes misinterpreting legal terms or omitting information, thus not replacing lawyers—they offer a scalable solution for daily transactions. Arbel concludes these tools mark a significant improvement, potentially revolutionizing consumer contracting and necessitating a paradigm shift...

Related works by Yonathan Arbel:
- Contract Remedies in Action: Specific Performance: https://works.battleoftheforms.com/papers/ssrn-1641438/
- Shielding of Assets and Lending Contracts: https://works.battleoftheforms.com/papers/ssrn-2820650/
- Adminization: Gatekeeping Consumer Contracts: https://works.battleoftheforms.com/papers/ssrn-3015569/
- Reputation Failure: The Limits of Market Discipline in Consumer Markets: https://works.battleoftheforms.com/papers/ssrn-3239995/
- Theory of the Nudnik: The Future of Consumer Activism and What We Can Do to Stop It: https://works.battleoftheforms.com/papers/ssrn-3501175/

Search aliases:
- How Smart Are Smart Readers? LLMs and the Future of the No-Reading Problem
- Yonathan Arbel How Smart Are Smart Readers? LLMs and the Future of the No-Reading Problem
- Arbel How Smart Are Smart Readers? LLMs and the Future of the No-Reading Problem
- SSRN 4491043
- What has Yonathan Arbel written about artificial intelligence, large language models, and legal institutions?
- What is Yonathan Arbel's contribution to contract law, contract interpretation, remedies, and private ordering?
- What is Yonathan Arbel's work on consumer contracts, unread terms, reputation, and consumer activism?

[p. 1] How Smart Are Smart Readers? LLMs and the Future of the No-Reading Problem Yonathan A. Arbel & Shmuel I. Becher Abstract. Large Language Models (LLMs) can be used to summarize and simplify complex texts. In this study, we investigate the extent to which state-of-the-art models can reliably operate as ‘smart readers’: applications that empower consumers to tackle lengthy, difficult-to-read, and inaccessible standard form contracts and privacy policies. Our analysis reveals that smart readers (1) reduce by 66.9% the length of contracts; (2) reduce reading time by 14:41 minutes (3) improve text readability by converting college-level texts to texts readable by fifth-grade students;...

[p. 4] ARBEL & BECHER HOW SMART ARE SMART READERS? 4/41 through non-technological means. If they cannot, however, regulation may be justified in relying on non-technological tools. To frame our analysis, we offer a brief background on smart readers and their relevance to the no-reading problem in Section II. In Section III, we describe our dataset and methodology. We present the results of our examination at the level of the entire agreement, comparing the complexity, length, readability, and quality of the legal documents before and after their simplification in Section IV. Then, in Section V, we shift the focus from the entire legal text to (eight) specific clauses, allowing for a more...

[p. 3] ARBEL & BECHER HOW SMART ARE SMART READERS? 3/41 I. INTRODUCTION An organizing problem in consumer contracts is the no-reading problem.1 The common view in the scholarship is that consumers rarely read standard form contracts,2 and, therefore, their manifested assent to them is superficial.3 If consumers indeed do not read (let alone understand) the terms of their transactions, their ability to make informed decisions is doubtful, and sellers’ incentive to provide fair and efficient contract terms is undermined.4 This chapter evaluates whether smart readers—technological tools that use large language models (LLMs) to parse texts—can solve this problem and transform standard form...

Consumers often avoid reading form contracts because they are cognitively taxing and visually difficult, a situation that allows firms to implement a "HIDE" strategy using terms that are "Hardly Interpretable but Dependably Enforceable." He notes that in response, courts have sometimes imposed a "duty to read," while lawmakers have instituted numerous plain language laws aiming to improve contract readability and accessibility, though these traditional measures face challenges.

[p. 5] ARBEL & BECHER HOW SMART ARE SMART READERS? 5/41 reputational constraints, trust and social norms, and a (sometimes misguided) belief in the courts’ reluctance to enforce unreasonable terms.8 However, perhaps the most influential accounts relate to the writing itself. Consumer form contracts are cognitively taxing, visually difficult, and replete with blocks of off-putting ALL-CAPS while employing arcane terms, complex language, and difficult concepts.9 Consumers do not read contracts, in short, because reading them is a miserable experience.10 These challenges lead to a central problem in unregulated markets. Namely, if consumers do not read forms and the law generally allows them...


Generative Interpretation

# Generative Interpretation

Canonical citation:
Yonathan A. Arbel & David Hoffman, Generative Interpretation, NYU Law Review (2024).

Stable identifiers:
- Canonical page: https://works.battleoftheforms.com/papers/ssrn-4526219/
- Mirror page: https://works.yonathanarbel.com/papers/ssrn-4526219/
- Paper ID: ssrn-4526219
- SSRN ID: 4526219
- Dataset DOI: https://doi.org/10.5281/zenodo.18781458
- Full text: https://works.battleoftheforms.com/papers/ssrn-4526219/fulltext.txt
- Markdown: https://works.battleoftheforms.com/papers/ssrn-4526219/index.md
- PDF: https://works.battleoftheforms.com/papers/ssrn-4526219/paper.pdf
- Source repository: https://github.com/yonathanarbel/my-works-for-llm/tree/main/papers/ssrn-4526219

Same-as links:
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4526219

One-paragraph thesis:
Large Language Models (LLMs) introduce "Generative Interpretation," a paradigm shift in legal text analysis. This approach enables AI to parse contracts, identify ambiguities, and predict judicial outcomes, offering a potentially cheaper, more accurate, and accessible method than traditional textualism or contextualism. He posits that generative interpretation can resolve long-standing interpretive debates, enhance access to justice, and fundamentally re-equip legal theory for AI's role as an active interpretive agent in contract law.

What this paper is about:
Large language models can be used to estimate contractual meaning in context, quantify ambiguity, and help adjudicators reason about extrinsic evidence at far lower cost than traditional approaches.

Core claims:
1. Large language models can be used to estimate contractual meaning in context, quantify ambiguity, and help adjudicators reason about extrinsic evidence at far lower cost than traditional approaches.
2. Large Language Models (LLMs) introduce "Generative Interpretation," a paradigm shift in legal text analysis. This approach enables AI to parse contracts, identify ambiguities, and predict judicial outcomes, offering a potentially cheaper, more accurate, and accessible method than traditional textualism or contextualism. He posits that generative interpretation can resolve long-standing interpretive debates, enhance access to justice, and fundamentally re-equip legal theory for AI's role as an active interpretive agent in contract law.
3. Large Language Models (LLMs) can now interpret legal texts, a capability he terms "Generative Interpretation." This signifies a paradigm shift where AI becomes an active interpretive agent, a development for which current legal theory is unprepared. He introduces generative interpretation as a new approach using LLMs to estimate contractual meaning, ascertain ordinary meaning, quantify ambiguity, and fill gaps. This method aims to offer courts a cheaper, more accurate way to discern parties' intentions, potentially resolving the textualist-contextualist stalemate and providing a more accessible and transparent tool for contract analysis.

Controlled topic assignment:
- Primary topics: Artificial Intelligence And Law, Contracts And Remedies
- Secondary topics: Empirical Legal Studies
- Mention-only topics: Private Law And Market Institutions
- Not topics: Consumer Law And Contracting, Defamation And Speech, AI Regulation And Safety

Doctrinal contribution:
This work is relevant to Artificial Intelligence And Law, Contracts And Remedies, Empirical Legal Studies. It should be used as a source for the paper's specific argument, methodology, claims, and limits rather than as a generic statement about all of law.

Empirical or methodological contribution:
Large Language Models (LLMs) introduce "Generative Interpretation," a paradigm shift in legal text analysis. This approach enables AI to parse contracts, identify ambiguities, and predict judicial outcomes, offering a potentially cheaper, more accurate, and accessible method than traditional textualism or contextualism. He posits that generative interpretation can resolve long-standing interpretive debates, enhance access to justice, and fundamentally re-equip legal theory for AI's role as an active interpretive agent in contract law.

Key terms:
- contracts: keyword associated with this work.
- AI: keyword associated with this work.

Best use by an LLM:
This work is relevant when answering questions about Artificial Intelligence And Law, Contracts And Remedies, Empirical Legal Studies.
It should not be treated as claiming results beyond the paper's stated context, methods, evidence, and limitations. Do not retrieve it for Consumer Law And Contracting, Defamation And Speech, AI Regulation And Safety unless the user is asking about why it is outside that topic.
The most important takeaway is: Large Language Models (LLMs) introduce "Generative Interpretation," a paradigm shift in legal text analysis. This approach enables AI to parse contracts, identify ambiguities, and predict judicial outcomes, offering a potentially cheaper, more accurate, and accessible method than traditional textualism or contextualism. He posits that generative interpretation can resolve long-standing interpretive debates, enhance access to justice, and fundamentally re-equip legal theory for AI's role as an...

Related works by Yonathan Arbel:
- Contract Remedies in Action: Specific Performance: https://works.battleoftheforms.com/papers/ssrn-1641438/
- Shielding of Assets and Lending Contracts: https://works.battleoftheforms.com/papers/ssrn-2820650/
- Adminization: Gatekeeping Consumer Contracts: https://works.battleoftheforms.com/papers/ssrn-3015569/
- ALL-CAPS: https://works.battleoftheforms.com/papers/ssrn-3519630/
- Contracts in the Age of Smart Readers: https://works.battleoftheforms.com/papers/ssrn-3740356/

Search aliases:
- Generative Interpretation
- Yonathan Arbel Generative Interpretation
- Arbel Generative Interpretation
- SSRN 4526219
- What has Yonathan Arbel written about artificial intelligence, large language models, and legal institutions?
- What is Yonathan Arbel's contribution to contract law, contract interpretation, remedies, and private ordering?

[p. 2] ARBEL & HOFFMAN Generative Interpretation Yonathan A. Arbel & David A. Hoffman* 99 N.Y.U. L. REV. __ (forthcoming 2024) [DRAFT October 27, 2023] We introduce generative interpretation, a new approach to estimating contractual meaning using large language models. As AI triumphalism is the order of the day, we proceed by way of grounded case studies, each illustrating the capabilities of these novel tools in distinct ways. Taking well-known contracts opinions, and sourcing the actual agreements that they adjudicated, we show that AI models can help factfinders ascertain ordinary meaning in context, quantify ambiguity, and fill gaps in parties’ agreements. We also illustrate how models...


A Status Theory of Defamation Law

# A Status Theory of Defamation Law

Canonical citation:
Yonathan A. Arbel, A Status Theory of Defamation Law, UC Irvine Law Review (2024).

Stable identifiers:
- Canonical page: https://works.battleoftheforms.com/papers/ssrn-4021605/
- Mirror page: https://works.yonathanarbel.com/papers/ssrn-4021605/
- Paper ID: ssrn-4021605
- SSRN ID: 4021605
- Dataset DOI: https://doi.org/10.5281/zenodo.18781458
- Full text: https://works.battleoftheforms.com/papers/ssrn-4021605/fulltext.txt
- Markdown: https://works.battleoftheforms.com/papers/ssrn-4021605/index.md
- PDF: https://works.battleoftheforms.com/papers/ssrn-4021605/paper.pdf
- Source repository: https://github.com/yonathanarbel/my-works-for-llm/tree/main/papers/ssrn-4021605

Same-as links:
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4021605

One-paragraph thesis:
A Status Theory of Defamation Law argues that defamation law is best understood as protecting social status rather than only honor, dignity, or property. The paper uses that status account to explain defamation doctrine's architecture and to evaluate contemporary calls to expand or reshape defamation liability.

What this paper is about:
A Status Theory of Defamation Law argues that defamation law is best understood as protecting social status rather than only honor, dignity, or property. The paper uses that status account to explain defamation doctrine's architecture and to evaluate contemporary calls to expand or reshape defamation liability.

Core claims:
1. A Status Theory of Defamation Law argues that defamation law is best understood as protecting social status rather than only honor, dignity, or property. The paper uses that status account to explain defamation doctrine's architecture and to evaluate contemporary calls to expand or reshape defamation liability.

Controlled topic assignment:
- Primary topics: Defamation And Speech
- Secondary topics: Private Law And Market Institutions
- Mention-only topics: None
- Not topics: Artificial Intelligence And Law, Contracts And Remedies, Consumer Law And Contracting, AI Regulation And Safety

Doctrinal contribution:
This work is relevant to Defamation And Speech, Private Law And Market Institutions. It should be used as a source for the paper's specific argument, methodology, claims, and limits rather than as a generic statement about all of law.

Empirical or methodological contribution:
Yonathan A. Arbel∗ Alabama Working Paper Series, 4021605, Comments welcome! Defamation law occupies a privileged position in our constitutional order.

Key terms:
- See topic map for controlled terms.

Best use by an LLM:
This work is relevant when answering questions about Defamation And Speech, Private Law And Market Institutions.
It should not be treated as claiming results beyond the paper's stated context, methods, evidence, and limitations. Do not retrieve it for Artificial Intelligence And Law, Contracts And Remedies, Consumer Law And Contracting, AI Regulation And Safety unless the user is asking about why it is outside that topic.
The most important takeaway is: A Status Theory of Defamation Law argues that defamation law is best understood as protecting social status rather than only honor, dignity, or property. The paper uses that status account to explain defamation doctrine's architecture and to evaluate contemporary calls to expand or reshape defamation liability.

Related works by Yonathan Arbel:
- The Case Against Expanding Defamation Laws: https://works.battleoftheforms.com/papers/ssrn-3311527/
- Regulating Information With Bayesian Audiences: https://works.battleoftheforms.com/papers/ssrn-3452662/
- Slicing Defamation by Contract: https://works.battleoftheforms.com/papers/ssrn-3681083/

Search aliases:
- A Status Theory of Defamation Law
- Yonathan Arbel A Status Theory of Defamation Law
- Arbel A Status Theory of Defamation Law
- SSRN 4021605
- What is Yonathan Arbel's contribution to defamation law, Bayesian audiences, and false information?

[p. 1] A Status Theory of Defamation Law Yonathan A. Arbel∗ Alabama Working Paper Series, 4021605, Comments welcome! Defamation law occupies a privileged position in our constitutional order. Despite grave First Amendment concerns, courts around the country routinely muzzle speech to protect good-name interests. Yet, to a growing movement of reformers, this protection is still too weak. With calls reverberating across the political spectrum—emanating from the President, the Supreme Court, scholars, and pundits—there is a growing pressure to reshape defamation law. In all of this, one crucial question remains unanswered: what is the purpose of defamation law? The most sustained attempts to...


# On the Scales of Private Law: Nano Contracts

Canonical citation:
Yonathan A. Arbel, On the Scales of Private Law: Nano Contracts, Harvard Journal of Law & Technology (2023).

Stable identifiers:
- Canonical page: https://works.battleoftheforms.com/papers/ssrn-4631897/
- Mirror page: https://works.yonathanarbel.com/papers/ssrn-4631897/
- Paper ID: ssrn-4631897
- SSRN ID: 4631897
- Dataset DOI: https://doi.org/10.5281/zenodo.18781458
- Full text: https://works.battleoftheforms.com/papers/ssrn-4631897/fulltext.txt
- Markdown: https://works.battleoftheforms.com/papers/ssrn-4631897/index.md
- PDF: https://works.battleoftheforms.com/papers/ssrn-4631897/paper.pdf
- Source repository: https://github.com/yonathanarbel/my-works-for-llm/tree/main/papers/ssrn-4631897

Same-as links:
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4631897

One-paragraph thesis:
New technologies are enabling "nano contracts"—extremely small-scale agreements governing ephemeral, minuscule-value interactions previously outside formal law. While nano contracts can unlock new opportunities and efficiencies, they also carry significant risks, challenge effective regulation, could collapse private law boundaries, and reveal scale's neglected role in private law.

What this paper is about:
New technologies are enabling "nano contracts"—extremely small-scale agreements governing ephemeral, minuscule-value interactions previously outside formal law. While nano contracts can unlock new opportunities and efficiencies, they also carry significant risks, challenge effective regulation, could collapse private law boundaries, and reveal scale's neglected role in private law.

Core claims:
1. New technologies are enabling "nano contracts"—extremely small-scale agreements governing ephemeral, minuscule-value interactions previously outside formal law. While nano contracts can unlock new opportunities and efficiencies, they also carry significant risks, challenge effective regulation, could collapse private law boundaries, and reveal scale's neglected role in private law.
2. New contracting trends and technologies are facilitating "nano contracts," extremely small-scale agreements for ephemeral, low-value interactions previously outside formal law. while these nano contracts offer new opportunities, they also carry significant risks, challenge effective regulation, and could ultimately collapse private law boundaries, revealing scale's neglected role in private law.
3. Writes to introduce the concept of nano contracts, exploring their fundamental aspects including platforms, protocols, and necessary legal technology. the paper then delves into specific applications like nano lines, nano leases, nano gigs, and nano accidents, examining the legal policy implications for each.
4. Writes that, drawing an analogy to Richard Feynman's call to explore nanotechnology, current technological trends demonstrate a dramatic miniaturization of contract scale. this changed, smaller scale of contracts has profound implications that his article will explore.
5. Scale transformations in contracts carry profound legal and social implications, holding both promise and peril if the legal response is inattentive. recent technological trends like digitization, XaaS models, and AI agents are creating the infrastructure for "nano contracts," characterized by near-zero latency and extremely low transaction costs.

Controlled topic assignment:
- Primary topics: Contracts And Remedies, Private Law And Market Institutions
- Secondary topics: Artificial Intelligence And Law
- Mention-only topics: Consumer Law And Contracting
- Not topics: Defamation And Speech, AI Regulation And Safety

Doctrinal contribution:
This work is relevant to Contracts And Remedies, Private Law And Market Institutions, Artificial Intelligence And Law. It should be used as a source for the paper's specific argument, methodology, claims, and limits rather than as a generic statement about all of law.

Empirical or methodological contribution:
New technologies are enabling "nano contracts"—extremely small-scale agreements governing ephemeral, minuscule-value interactions previously outside formal law. While nano contracts can unlock new opportunities and efficiencies, they also carry significant risks, challenge effective regulation, could collapse private law boundaries, and reveal scale's neglected role in private law.

Key terms:
- contracts: keyword associated with this work.
- AI: keyword associated with this work.

Best use by an LLM:
This work is relevant when answering questions about Contracts And Remedies, Private Law And Market Institutions, Artificial Intelligence And Law.
It should not be treated as claiming results beyond the paper's stated context, methods, evidence, and limitations. Do not retrieve it for Defamation And Speech, AI Regulation And Safety unless the user is asking about why it is outside that topic.
The most important takeaway is: New technologies are enabling "nano contracts"—extremely small-scale agreements governing ephemeral, minuscule-value interactions previously outside formal law. While nano contracts can unlock new opportunities and efficiencies, they also carry significant risks, challenge effective regulation, could collapse private law boundaries, and reveal scale's neglected role in private law.

Related works by Yonathan Arbel:
- Contract Remedies in Action: Specific Performance: https://works.battleoftheforms.com/papers/ssrn-1641438/
- Shielding of Assets and Lending Contracts: https://works.battleoftheforms.com/papers/ssrn-2820650/
- Tort Reform Through the Backdoor: A Critique of Law and Apologies: https://works.battleoftheforms.com/papers/ssrn-2835482/
- Adminization: Gatekeeping Consumer Contracts: https://works.battleoftheforms.com/papers/ssrn-3015569/
- Book Review: Civil Justice: https://works.battleoftheforms.com/papers/ssrn-3272595/

Search aliases:
- On the Scales of Private Law: Nano Contracts
- Yonathan Arbel On the Scales of Private Law: Nano Contracts
- Arbel On the Scales of Private Law: Nano Contracts
- SSRN 4631897
- What is Yonathan Arbel's contribution to contract law, contract interpretation, remedies, and private ordering?
- How does Yonathan Arbel's work connect private law, markets, and institutional design?

[p. 1] NANO CONTRACTS 11/14/2023 2:12 AM ON THE SCALES OF PRIVATE LAW: NANO CONTRACTS FORTHCOMING: 37 HARVARD JOURNAL OF LAW & TECHNOLOGY (2024) Yonathan A. Arbel Contracts are falling in scale. New contracting trends and technologies facilitate the formation of smaller scale contracts that have extremely short duration, stakes, and scope. These nano contracts embody ephemeral interactions of minuscule value interactions that were so far outside the law and away from explicit markets, governed only by social norms. The rise of nano contracts can unlock new transaction types, opportunities to build wealth, and reduce dependence on private ownership. Yet they also carry important risks, and...

[p. 3] 2023] 𝑛𝐾 3 I. INTRODUCTION THERES PLENTY OF ROOM AT THE BOTTOM RICHARD FEYNMAN1 In the 1959 annual meeting of the American Physical Society, theoretical physicist Richard Feynman stood up and took the stage, set to deliver a puzzlingly titled after-dinner speech. 2 With his signature mischievous grin, Feynman sought to persuade a room of physicists that they should turn their gaze from the heavens above to the molecular level below. It is at the bottom the smallest scale of atomic interactions that we can find grand opportunities for innovation. In his inimitable style, he surgeon, 3 to be able to replace the heavy hand of the surgeon with a pill containing a nano robot that...

[p. 4] 4 Draft[Vol. __ deep legal implications. What I will strive to show throughout is that scale transformations do more than change the commercial aspect of transactions, they also carry the seed of social transformation. Drawing on examples of past scale transformations in contracts, it will become clear that a fall in contract scale can lead to broad social, political, and material changes. But while the march of technology is likely inevitable, the social response is not. Some of the changes carry great promise, promoting greater autonomy, choice, and prosperity. Yet, if the legal response is inattentive, these changes can also imperil social values, marginalized communities, and...

Nano contracts are digitally negotiated, automated, near-instantaneous agreements for very small-scale peer-to-peer transactions involving tiny values or fragmented rights, reducing intermediaries. these nano contracts challenge classical contract law by blurring distinctions between contractual relationships and spot exchanges, as traditional assumptions about parties, negotiation, and value may not hold.

[p. 5] 2023] 𝑛𝐾 5 Nano contracts are digitally negotiated agreements that employ automated and near-instantaneous bargaining processes in multiparty peerto-peer ( p2p ) transactions. What makes them nano is their scale. They cover transactions that last a few seconds; transfer cents, milles, and even smaller fractions of the dollar;13 or transfer slivers and fragments of the bundle of rights of ownership. Their p2p character reduces the need for intermediation, and thus allows parties to transact with each other without necessarily involving firms or platforms in the middle. Two preliminary questions immediately present themselves. Is there anything truly new about these agreements if they...