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...
Yonathan A. Arbel, The Generative Reasonable Person, BYU Law Review (2026).
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.
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.
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.
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.
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
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.
Yonathan A. Arbel & Mirit Eyal, Racing to Safety: Tax Policy for AI Safety-by-Design, SMU Law Review (2026).
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.
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.
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.
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.
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.
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...
Yonathan A. Arbel, Judicial Economy in the Age of AI, Colorado Law Review (2025).
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.
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.
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.
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.
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.
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...
Yonathan A. Arbel & Michael D. Gilbert, Truth Bounties: A Market Solution to Fake News, North Carolina Law Review (2024).
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.
Yonathan A. Arbel, Time and Contract Interpretation, Research Handbook on Law and Time (2024).
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.
Yonathan A. Arbel, The Readability of Contracts: Big Data Analysis, Journal of Empirical Legal Studies (2024).
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.
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...
Yonathan A. Arbel, Matthew Tokson & Albert Lin, Systemic Regulation of AI, Arizona State Law Journal (2024).
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.
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.
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.
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.
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.
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...
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).
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.
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.
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.
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.
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.
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.
Yonathan A. Arbel & David Hoffman, Generative Interpretation, NYU Law Review (2024).
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.
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.
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.
Yonathan A. Arbel, A Status Theory of Defamation Law, UC Irvine Law Review (2024).
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.
Yonathan A. Arbel, On the Scales of Private Law: Nano Contracts, Harvard Journal of Law & Technology (2023).
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.
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.
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.
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.
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.
Defamation with Bayesian Audiences analyzes how strictly law should regulate false defamatory statements when audiences update their beliefs in response to legal rules and judicial error. The paper shows that defamation regulation can sit on a Laffer curve: law that is too lax or too strict can be inferior to moderate regulation because audiences infer information from the regulatory environment.
Yonathan A. Arbel & Murat C. Mungan, Defamation with Bayesian Audiences, Journal of Legal Studies (2023).
What does it mean to have machines that can read, explain, and evaluate contracts? Recent advances in machine learning have led to a fundamental breakthrough in machine language models, portending a profound shift in the ability of machines to process text. Such a shift has far-reaching consequences for diverse areas of law, which are predicated on, and justified by, the existence of information barriers. Our object here is to provide a general framework for evaluating the legal and policy...
Yonathan A. Arbel & Shmuel I. Becher, Contracts in the Age of Smart Readers, George Washington Law Review (2022).
AI-powered "smart readers" represent a significant breakthrough in contract analysis, capable of simplifying, personalizing, and benchmarking terms for consumers. While offering profound benefits like increased understanding, improved market competition, and enhanced access to justice, these tools also introduce serious risks such as errors, bias, adversarial exploitation, and discrimination. Arbel calls for a new legal and regulatory framework to navigate these complex implications, as current doctrines are unprepared for this technological shift and its impact on contract law and consumer protection.
AI-powered "smart readers" are emerging from machine learning breakthroughs, poised to disrupt the "dismal equilibrium" where consumers ignore complex contract terms. these tools can simplify, personalize, interpret, and benchmark contracts, offering a technological solution to information barriers. His work explores their capabilities, potential uptake, and broad implications for contract law, including market competition, errors, access to justice, and discrimination, highlighting the need for new regulatory responses as current legal doctrines are unprepared for these advancements and their associated risks like bias and exploitation.
Smart readers, powered by AI like GPT-3, possess core capabilities crucial for consumer empowerment: simplification of complex legal text, personalization to individual user needs (including linguistic and cognitive adaptations), construction of contractual meaning through explanations, and benchmarking contracts against market alternatives. these tools can make obscure clauses understandable, provide scores for privacy policies, and allow interactive questioning, offering advantages in cost, speed, and accessibility over human lawyers, thereby helping consumers comprehend fine print and understand market choices.
Consumer uptake of smart readers, while potentially limited by reading aversion and cognitive biases, could significantly impact markets even with modest adoption by an "informed minority," fostering term competition. these tools can act as behavioral nudges, countering cognitive overload by summarizing complex information, addressing myopia by highlighting risks like warranties, and helping consumers overcome price manipulations. The success or failure of uptake will offer insights into theories on why consumers don't read contracts, with quality, cost, and user experience being critical factors.
Smart readers carry significant risks, including errors (isolated or correlated), which must be evaluated against human error rates. more pernicious are adversarial attacks, where firms use subtle textual manipulations to mislead AI, and the potential for discrimination, as firms might offer inferior terms to non-users or leverage smart reader data for redlining. There's also a risk of overcompliance if smart readers don't distinguish unenforceable terms, and bias within the AI models themselves, necessitating caution despite their potential.
A small group of hyper-persistent consumers, dubbed "nudniks," play a crucial role in market discipline by actively challenging seller misconduct, benefiting all consumers. However, sellers increasingly use big data to identify and neutralize these nudniks, undermining accountability. Arbel calls for legal strategies to protect this vital "nudnik-based activism" and preserve its positive spillovers, shifting focus from mythical contract-readers to these real-world enforcers of market norms.
Yonathan A. Arbel & Roy Shapira, Theory of the Nudnik: The Future of Consumer Activism and What We Can Do to Stop It, Vanderbilt Law Review (2020).
"nudniks" are hyper-persistent consumers with idiosyncratic utility functions who challenge seller misconduct *ex post*, even when costs outweigh benefits, acting where most remain passive.
Distinct from passive consumers or pre-purchase focused "shoppers," nudniks are driven by fairness, spite, or ideology, often being derided despite their crucial role.
Nudniks act as engines of market discipline, generating positive spillovers by publicly airing grievances, litigating, and creating reviews, thus deterring unfair practices and enforcing market norms benefiting all.
Their actions solve collective action problems, provide monitoring for sellers, and can compel broad policy changes, enhancing both legal and reputational deterrence for the passive majority.
Firms leverage detailed consumer data to predict complaint likelihood and then disarm nudniks through tailored remedies, by muffling their voices, or by avoiding or "gagging" them.
Slicing Defamation by Contract is a short essay at the intersection of defamation, speech, and private ordering. The current corpus extraction for this paper is incomplete and comment-heavy, so this record is intentionally conservative: use it as a pointer to the paper page and PDF, not as a source for detailed claim extraction until the text is repaired.
Yonathan A. Arbel, Slicing Defamation by Contract, Chicago Law Review Online (2020).
[p. 1] W03_ARBEL_GRAPHICS_REVISED.DOCX (DO NOT DELETE) 1/30/20 11:10 AM REPUTATION FAILURE: THE LIMITS OF MARKET DISCIPLINE IN CONSUMER MARKETS Yonathan A. Arbel* Many believe that consumer-sourced reputational information about products should increasingly replace topdown regulation. Instead of protecting consumers through coercive laws, reputational information gleaned from the wisdom of the crowd would guide consumer decision-making. There is now a growing pressure to deregulate in diverse...
Yonathan A. Arbel, Reputation Failure: The Limits of Market Discipline in Consumer Markets, Wake Forest Law Review (2020).
Consumer-sourced reputation systems, widely believed to replace formal regulation, suffer from inherent "Reputation Failure." Due to the public-good nature of reviews and misaligned incentives, these systems produce systematically distorted information (e.g., sluggishness, extreme reviews). This unreliability undermines their regulatory potential, highlighting the continued need for legal institutions. Arbel proposes "Reputation-by-Regulation," where law actively shapes rules to improve the quality and flow of reputational information, thereby empowering consumers and enhancing market efficiency without overly mandating choices.
The Inadequacy of Unregulated Reputation and Call for Intervention: consumers struggle with the scale of qualitative review analysis and detecting sophisticated fakes; any heuristics they develop are exploitable. Distorted peer-to-peer information leads to "reputation failures," undermining arguments for deregulation by causing persistent consumer mistakes and negative market dynamics akin to a "lemon market." Modern deregulation debates often overlook these systematic failures, highlighting the need for legal interventions to facilitate quality reputational information and temper unjustified deregulatory trends.
Introducing "Reputation-by-Regulation" and Addressing Platform Issues: law can actively design rules ex ante to make market information more reliable and abundant through "Reputation-by-Regulation," where legal institutions influence reputation. While platforms like Amazon act as metaregulators, their policing is limited by contractual reliance and conflicts of interest. Platforms may lack incentives to act in the public interest, potentially manipulating markets or censoring reviews (as alleged against Uber, Yelp, Amazon), a problem worsened by court rulings granting them broad curatorial discretion over user-generated content.
Information regulation often overlooks how audiences adjust their beliefs and actions based on the strictness of laws governing statement veracity. His research aims to address this "audience gap" by using a Bayesian game to model interactions between speakers, targets, and audiences, particularly examining how legal strictness impacts their behavior and the resulting information landscape.
Yonathan A. Arbel & Murat C. Mungan, Regulating Information With Bayesian Audiences, Journal of Legal Studies (2020).
The common approach to regulating information exchange has a significant blind spot: it often fails to account for the dynamic ways in which audiences adapt. Specifically, how audiences adjust their beliefs and subsequent actions is directly influenced by the perceived strictness of the laws that govern the truthfulness of statements. This oversight can lead to miscalibrated regulations.
Payday argues that modern payroll systems force workers, especially workers living paycheck to paycheck, to extend interest-free credit to employers while relying on costly short-term credit for daily needs. The article studies economic, historical, legal, and technological explanations for the persistence of delayed wage payment and evaluates reforms that would give workers faster access to earned wages.
Yonathan A. Arbel, Payday, Washington University Law Review (2020).
Traditional consumer protection, thought to rely on an "informed minority" reading contracts, is ineffective. Instead, a new type of activist, the "nudnik" or "crusading minority," drives market discipline. Motivated by moral outrage and a sense of justice rather than contract details, nudniks use public shaming, complaints, and lawsuits to punish firms for perceived wrongdoings. These actions create broad benefits, making nudniks the "unsung heroes" of consumer markets, even if their...
Yonathan A. Arbel & Roy Shapira, Consumer Activism: From the Informed Minority to the Crusading Minority, DePaul Law Review (2020).
The long-held "informed minority theory," suggesting a few knowledgeable consumers discipline markets by reading contracts, is unrealistic and empirically challenged. He notes consumers are often rationally apathetic or unable to understand complex terms, leading to passivity. Furthermore, theories of reputational discipline through online reviews are criticized as underspecified, failing to explain how credible information is produced or disseminated, and often relying on biased or low-participation data. These traditional models inadequately explain consumer power in modern markets, prompting a search for alternative explanations.
A new "crusading minority," termed "nudniks," is emerging as a key force in consumer governance. Unlike the supposed "informed minority," nudniks are motivated less by informing others and more by moral outrage, seeking to punish firms for perceived wrongdoings. They operate *ex post*, reacting to disappointments through complaints, reviews, and litigation, often leveraging the internet. This nudnik-driven activism challenges traditional theories by suggesting that consumer power can be effective even when consumers don't read contracts, as sellers are incentivized to improve quality for all to avoid public disputes.
"nudniks"—consumers highly prone to vindicating their rights—are distinct from other archetypes. "Passivists," the largest group, are largely inactive. "Shoppers" meticulously compare terms *ex ante* and primarily "exit" to competitors. "Sophisticates" strategically leverage contract terms for personal profit based on cost-benefit analysis. In contrast, nudniks act *ex post* out of principle or moral outrage, often when others would not, and their actions are more likely to generate positive spillovers for all consumers, whereas sophisticates primarily seek private gains that may not benefit, or could even harm, others.
Nudniks are driven by an innate sense of justice or idiosyncratic motivations, leading them to act even when costs seem to outweigh benefits for an average consumer. They employ tactics like complaints, reviews, lawsuits, and viral social media campaigns (e.g., "United Breaks Guitars") to highlight seller failures. This activism, based on broad transactional expectations rather than specific contract terms, can be initiated by a single individual and amplified by social media. Sellers respond due to legal and reputational risks, often improving service for all, making nudniks effective at solving collective action problems.
While nudnik activism is potent, it's not without concerns. Critics question if complaints are always representative or beneficial, potentially being frivolous or driven by unrealistic expectations. However, he argues these issues may be overstated. Nudniks are a heterogeneous group, and their actions, even if driven by "spite," often highlight genuine consumer interests and can lead to market-wide improvements. The impact of unrepresentative nudniks is also mitigated as other consumers tend to amplify only those grievances they find valid, and sellers actively manage expectations.
A hallmark of consumer contracts is long blocks of capitalized text. Courts and legislators believe that such “all-caps” clauses improve the quality of consumer consent and thus they will often require the capitalization of certain key terms in consumer contracts. Some of the most important terms in consumer contracts—warranty disclaimers, liability releases, arbitration clauses, and automatic subscriptions—will be enforced only because they appeared in all-caps in the contract. This Article...
Yonathan A. Arbel, ALL-CAPS, Journal of Empirical Legal Studies (2020).
The widespread legal practice of using all-caps in consumer contracts to ensure key terms are conspicuous and consent is improved is deeply flawed. His empirical research demonstrates that all-caps text fails to enhance consumer understanding, provides no benefits for most readers, and significantly harms the comprehension of older individuals. Arbel calls for abandoning this unsubstantiated tradition and exploring more effective disclosure methods.
Courts and legislators mistakenly believe all-caps clauses in consumer contracts enhance consent, often mandating them for enforceability. He asserts this practice is a deeply misguided instance of "contract lore," an ungrounded belief among lawyers, especially problematic given consumers often don't read fine print. Arbel argues that if all-caps doesn't improve consent, or worsens it, courts may be wrongly enforcing harsh terms based on an illusion of understanding, depriving consumers of recourse. This legal tradition lacks empirical support for its effectiveness.
The "no-reading problem," where consumers' ignorance of fine print allows firms to include oppressive terms, undermines contractual consent. A common solution adopted by courts and legislators, such as the UCC's requirement for conspicuous warranty waivers, is to mandate the prominent display of important terms. This strategy aims to improve consumer consent by signaling the importance of key provisions and making them more accessible, with all-caps being a widely endorsed method to achieve such conspicuousness and thereby enhance enforceability.
The legal convention of using all-caps to denote consent lacks empirical support; early psychological studies indicating it impeded reading were overlooked. His new research analyzing 500 popular consumer contracts from highly visited websites like Google and Amazon reveals its pervasiveness, with over 77% containing at least one fully capitalized paragraph and 9% of all words capitalized. This prevalence in contracts affecting most American adults is troubling, as the policy is based on speculation rather than evidence of its actual effectiveness.
Experimental Findings: All-Caps Fails to Improve Consent and Harms Older Readers
Expanding defamation law is misguided. He contends that such expansions overlook crucial "audience effects," where stricter laws can paradoxically harm reputations by making any remaining false statements appear more credible. This increased believability means attempts to fight "fake news" by strengthening defamation law could backfire. Arbel challenges the fundamental assumption that defamation law inherently protects reputation, suggesting it can even undermine it by altering how audiences...
Yonathan A. Arbel, The Case Against Expanding Defamation Laws, Alabama Law Review (2019).
His co-authored article, "The Case Against Expanding Defamation Law," examines the objectives and workings of defamation law, its relationship with reputation, and, critically, how "audience effects"—the way audiences perceive statements—shape the law's effectiveness and its appropriate boundaries.
The widely held belief that defamation law safeguards reputation is flawed because it ignores "audience effects"—how the law shapes audience perception. He argues that stricter defamation laws can paradoxically damage reputations by making statements seem more reliable, implying that expanding these laws to combat fake news could prove counterproductive.
Concurrent political pressures to bolster libel laws, exemplified by figures like then-President Trump, and a new American Law Institute project on defamation, signal potentially imminent, significant changes to the law. He notes these calls for expansion, involving reduced safeguards and higher damages, are largely based on the prevailing theory that defamation law's primary function is reputation protection.
The prevalent "reputation-protection theory" suggests stricter defamation laws, by penalizing falsehoods, protect reputations from damaging comments. His article, however, critiques this theory, highlighting its failure to account for significant "audience effects" – how the audience's perception of statements is influenced by the legal framework itself.
Traditional defamation law is deficient as it neglects the "audience effect," meaning harm isn't direct but mediated by third-party perception of negative statements. Applying signaling theory, he explains that stricter defamation laws can enhance the perceived reliability of statements, because the higher cost of falsehoods (due to increased legal risk) makes these "signals" appear more credible than "cheap talk."
While Professor Croley's *Civil Justice Reconsidered* aptly describes the civil justice crisis of cost and inaccessibility, its diagnosis of under-participation by meritorious plaintiffs is not empirically proven and its reliance on win rates is misleading. Arbel contends Croley's proposed reforms, like increasing case volume, would overwhelm the system, especially concerning the neglected crisis in debt collection. He also critiques Croley's tort reform ideas and civil "Gideon" proposal,...
Yonathan A. Arbel, Book Review: Civil Justice, Civil Justice Quarterly (2018).
Writes about his "Adminization" proposal, where a governmental agency audits court cases and levies fines, creating cost-effective, scalable consumer protection. He observes that tort reform has shapeshifted, with strategies like apology laws acting as covert reform. While Croley's tort reform proposals are sensible, Arbel argues they don't address new frontiers like tortfeasors using strategic apologies. Finally, he suggests Croley's recommendation for a civil "Gideon" right, mandating subsidized lawyering for indigent plaintiffs, may prove counter-productive.
The current consumer debt litigation system is failing due to prevalent unmeritorious claims and consumer inability to defend themselves. He proposes "Adminization," where an administrative agency acts as a cost-effective gatekeeper, using sampling and AI to audit lawsuits and levy large fines against those filing baseless claims. This aims to deter abuse, enhance judicial efficiency, and provide meaningful oversight, offering a more just and economically viable solution than existing...
Yonathan A. Arbel, Adminization: Gatekeeping Consumer Contracts, Vanderbilt Law Review (2018).
Because large companies and debt collectors file numerous unmeritorious claims against consumers who lack resources to defend themselves, leading to millions of default judgments and rendering courts ineffective, he proposes "Adminization"—an administrative agency acting as a cost-effective gatekeeper that samples cases and levies large fines for baseless claims to deter such wrongful behavior.
An introductory anecdote about Margaret Donnelly, an elderly widow facing severe legal repercussions for a small, unnotified debt, illustrates a systemic problem where vulnerable individuals are caught off-guard by consumer credit legal actions, highlighting a fundamental flaw in the justice process for these cases.
A significant crisis exists with millions of consumers facing abusive debt collection lawsuits, many lacking merit or concerning already settled debts, particularly targeting those unable to afford legal representation; this is worsened by consumer difficulties in accessing courts, resulting in numerous default judgments with minimal judicial oversight.
The current system for consumer debt disputes is broken, evidenced by penalties against financial institutions for filing false affidavits and pursuing fraudulent debts; to combat this, he proposes "Adminization," where a gatekeeper administrative agency would audit a sample of debt collection cases pre-litigation and issue large fines for fraud, deterring wrongful behavior.
Under his proposal, consumer protection agencies would be notified of all incoming lawsuits, using administrative powers to audit a small fraction and fine abusers. Case selection would initially be random to ensure all creditors face potential detection, similar to IRS practices, but could later be enhanced by machine learning to target statistically high-risk cases.
Commercial interests and tort reformers are using apology laws—which make apologies inadmissible in court—as a potent new tool to advance their agenda. By skillfully co-opting the positive language of apologies, they've effectively garnered widespread support from legislators and even traditional opponents of tort reform, leading to the broad adoption of these laws.
Yonathan A. Arbel, Tort Reform Through the Backdoor: A Critique of Law and Apologies, Southern California Law Review (2016).
Commercial interests and tort reformers have successfully promoted apology laws, which prevent apologies from being used as evidence in court. He identifies these laws as a new and powerful platform for these groups to advance their specific agendas. further writes that this strategic approach has been remarkably effective. By framing these laws using the appealing language of apologies, proponents have managed to gain support from lawmakers and, significantly, even from those who typically oppose tort reform, resulting in the widespread enactment of such legislation.
The primary means of enforcement of legal liabilities is through the seizure of debtors’ assets. However, debtors can shield their assets in various ways and thereby reduce the power of enforcement. This paper studies the circumstances under which a debtor would choose to shield assets and the value of assets that would be shielded. A key idea is that borrower’s wealth mutes shielding incentives. Intuitively, avoiding debts through shielding requires that enough assets will be shielded, for...
Yonathan A. Arbel, Shielding of Assets and Lending Contracts, International Review of Law & Economics (2016).
A debtor's wealth significantly influences their decision to shield assets, as wealthier individuals would need to shield a larger volume of assets at greater cost for it to be effective against creditors. his paper develops a theory of asset shielding to explain this behavior, arguing that richer debtors often find it irrational to shield, while poorer debtors pose a higher shielding risk which impacts credit markets.
How is a right to specific performance of a contract used by parties? Despite longstanding scholarly interest in the topic, this question has been largely left unexplored. This Article presents a qualitative study of parties and attorneys involved in specific performance litigation. It investigates how parties choose between remedies, whether they negotiate after judgment for specific performance, whether specific performance is implemented, and the difficulties involved in its implementation....
Yonathan A. Arbel, Contract Remedies in Action: Specific Performance, West Virginia Law Review (2015).
Specific performance in contract law is often less effective and less frequently sought than theories suggest. His qualitative study in Israel reveals parties avoid it due to enforceability issues, lawyer agency problems, and changing preferences. When pursued, motivations include signaling or post-judgment renegotiation. Findings show practical enforcement difficulties, especially for unique goods, and challenge assumptions in both rights-based and economic theories, highlighting a gap between legal doctrine and real-world litigant behavior and outcomes, necessitating a re-evaluation of its role and efficacy.
Parties often avoid suing for specific performance due to low enforceability, lawyer agency issues, and evolving preferences. When they do seek it, their reasons can extend beyond simply wanting performance, including signaling case strength, aiming for quicker and less costly resolutions, or strategically positioning themselves for post-judgment renegotiation.
His qualitative study shows many plaintiffs choose damages over specific performance due to its difficult execution, especially for unique goods with unclear standards, and sometimes because lawyers prefer damages for easier fee collection. He notes these practices often contradict established theories, as plaintiffs may resist commodifying judgments (challenging economic views) or use the remedy instrumentally for non-performance goals (challenging rights-based views).
Contract law features a core debate between economic analysis, focused on efficiency and welfare, and rights-based theories, centered on morality. These perspectives justify remedies differently: rights-based theories often favor specific performance to uphold promises, while economic theories see remedies instrumentally. He notes both frequently neglect the impact of a victim’s choice among available remedies.
Despite growing empirical work on contract remedies, there's a notable gap in understanding parties' internal views, expectations, and behaviors regarding specific performance. His article addresses this through a qualitative study with litigants and lawyers in Israel, chosen for its legal similarity to the U.S. but with specific performance as the default remedy, offering a unique comparative context.