Kaal on From Neoclassical to Computative Labor: Foundations for a Testable Theory of Reputation Governance in the Agent Economy

Wulf A. Kaal (U St. Thomas Law (Minnesota)) has posted “From Neoclassical to Computative Labor: Foundations for a Testable Theory of Reputation Governance in the Agent Economy” on SSRN. Here is the abstract:

Artificial agents are becoming economic actors, performing open-ended cognitive work at a marginal cost that approaches zero. Neoclassical economics, built on scarce human labor allocated by price, does not describe this regime. This paper sets out the foundations of an alternative and argues that the alternative is empirically testable now. We distinguish the neoclassical labor force (NCLF) and neoclassical labor market (NCLM) from their computative counterparts, the computative labor force (CELF) and computative labor market (CELM), in which the binding coordination constraint is accumulated reputation rather than scarcity-driven price. From a synthesis of Arrow’s impossibility theorem, the Folk Theorems of repeated games, and incomplete-contract theory, we restate a foundational result: any fixed governance rule set is eventually dominated, so coordination among autonomous agents requires institutions that govern their own evolution, with reputation as the operative signal. We then advance the methodological claim that motivates the paper. The distinction between neoclassical and computative labor is studiable today, because capable agents, on-chain coordination substrates, and reputation primitives already exist, and the competing predictions of the two accounts are falsifiable in controlled multi-agent settings. We situate the argument within a sixpaper research arc that substantiates these foundations, and we state the propositions the arc evaluates. The governance mechanism and the empirical results are developed elsewhere in the arc and are deliberately outside the scope of this paper.

Jurcys on Copyright Registration Requirement in the U.S.

Paul Jurcys (U California) has posted “Copyright Registration Requirement in the U.S.” on SSRN. Here is the abstract:

This entry, prepared for the Elgar Encyclopedia of Intellectual Property Law (2026), provides an overview of copyright registration requirements in the United States. It explains that, unlike patents or trademarks, copyright protection in the U.S. arises automatically upon the creation of an original work fixed in a tangible medium. Registration with the U.S. Copyright Office is therefore optional for obtaining protection but essential for enforcement and evidentiary purposes. The entry traces the historical evolution of copyright formalities—from the 1790 Act’s mandatory filings to the modern system under the 1976 Act—and outlines the procedures, functions, and benefits of registration, including access to statutory damages, attorney’s fees, and prima facie evidence of ownership. It concludes with ongoing debates on formalities, modernization, and AI-related challenges.

Sachdeva & Kolt on Why AIs (Might) Obey the Law

Pratik Sachdeva (UC Berkeley) and Noam Kolt (Hebrew U) have posted “Why AIs (Might) Obey the Law” on SSRN. Here is the abstract:

AI models are no longer confined to producing content and increasingly operate as agents that take actions on behalf of users. A growing body of work empirically tests whether AI models when acting as agents comply with or violate applicable law, including corporate law, tort law, labor law, property law, and contracts. In this paper, we explore a related question: examining why AI models might obey the law. To this end, we draw on and extend the methods for measuring legal compliance pioneered in Tom Tyler’s seminal work, Why People Obey the Law (1990, 2006). Across three studies, we adapt Tyler’s survey methodology—which was originally devised to study the factors explaining human subjects’ compliance with law—to nine AI models. We elicit the AI models’ reported legal compliance alongside the four factors that Tyler proposed to explain compliance: deterrence, morality, peer disapproval, and obligation to obey the law. In Study 1, we find that, when situated as human respondents, AI models report largely homogeneous attitudes toward legal compliance that are broadly comparable to the average human respondent in Tyler’s studies, with one exception: obligation to obey the law diverges sharply across different AI models. In Study 2, we find that demographic conditioning—situating AI models with a particular background (e.g., race, gender)—substantially alters their attitudes toward law, often exaggerating associations Tyler observed in humans and sometimes reproducing stereotyped patterns. In Study 3, we investigate why AI models might themselves obey the law when performing tasks that AI models can undertake in practice. We find that AI models uniformly report near-complete compliance with law, but their attitudes toward law vary substantially: some AI models express a strong sense of obligation to comply with law, while others express a more neutral attitude toward law. Taken together, our methods and results lay the foundation for interrogating the legal compliance of contemporary AI models, as well as shaping the development of future models and their relationship to law.

Blair-Stanek et al. on Is AI’s Law School Exam Performance Plateauing?

Andrew Blair-Stanek (U Maryland Francis King Carey Law) et al. have posted “Is AI’s Law School Exam Performance Plateauing?” on SSRN. Here is the abstract:

Last spring, we had OpenAI’s reasoning model o3 take our final exams, with the reasoning effort parameter set to “high,” and graded its answers on the same curve as our students. o3 got grades ranging from A+ to B. This spring, we repeated the experiment, using OpenAI’s latest reasoning model, GPT-5.5, with the reasoning effort at the new “xhigh” setting. GPT-5.5 got two A+s, three As, two As , and a B+, a good performance but far short of superhuman. Depending on the metric, GPT-5.5 may have actually performed worse than o3 did last year, despite the new “xhigh” setting. These results may fit the broader pattern of frontier AI models’ performance plateauing on other legal benchmarks.

Ferguson on Personal Medical AI: A Framework for Individual-Based Healthcare Monitoring SubTitile: Personal Medical AI Framework

John Ferguson (The Ferguson Clinic) has posted “Personal Medical AI: A Framework for Individual-Based Healthcare Monitoring SubTitile: Personal Medical AI Framework” on SSRN. Here is the abstract:

Current healthcare AI systems compare patient data against population norms, potentially missing clinically significant deviations that are abnormal for specific individuals. We propose a framework for personal medical AI that establishes individual baselines, learns patient-specific patterns, and detects deviations meaningful to each patient rather than comparing against population averages. This paradigm shift from population-based to individual-based monitoring requires addressing technical architecture, clinical integration, the radical transparency problem, impacts on the doctor-patient relationship, and equity concerns. Personal medical AI represents not a replacement for clinical care but a transformation of the patient-AI-clinician relationship that requires careful implementation to preserve therapeutic value while enabling unprecedented longitudinal insight.

Ard et al. on Technology Law Chapter 6: Upset Equilibria

Bj Ard (U Wisconsin Law) and Rebecca Crootof (U Richmond Law) have posted “Technology Law Chapter 6: Upset Equilibria” on SSRN. Here is the abstract:

Based on years of experience teaching the subject, we have produced a first draft of a “Technology Law” coursebook. It teases out fundamental concepts, introduces our methodology for resolving tech-fostered legal uncertainties, and identifies the strengths and weaknesses of different regulatory choices. Through a mixture of readings, exercises, and discussion questions, it helps readers develop facility in:

– Recognizing the recurring techlaw and policy questions and discerning the application, normative, and institutional uncertainties associated with a particular technology;

– Working through the process of resolving legal uncertainties, which includes consciously selecting a regulatory approach, identifying legally salient characteristics and relevant analogies, and weighing the benefits and drawbacks of various regulatory choices (law-by-analogy, creating new law, or reconfiguring legal institutions); and

– Developing familiarity with employing and countering common rhetorical strategies for advancing, opposing, or shaping regulation.

This course is designed to be accessible and useful to all students, regardless of career interests or prior experience with technology. New technologies challenge every area of the law, and the regulatory and rhetorical strategies we’ll explore are transferable across subjects.

This posting includes Chapter Six: Upset Equilibria. Future chapters will be posted bi-monthly.

We welcome feedback at the link included in the document; additional chapters will be updated regularly. If you are interested in teaching from this text, in whole or in part, please let us know, as we would be happy to share our class notes and slides.

Veale et al. on The Obligations of Providers of General-Purpose AI Models

Michael Veale (U College London Laws) and João Pedro Quintais (U Amsterdam Institute Information Law (IViR)) have posted “The Obligations of Providers of General-Purpose AI Models” on SSRN. Here is the abstract:

During the legislative process, the EU Artificial Intelligence (AI) Act was amended to include provisions related to general-purpose AI (GPAI) models. These broadly relate to transparency towards downstream users and relevant regulators, in addition to obligations connected to intellectual property. In this paper, we provide detailed analysis of these new provisions in the context of current technological applications and emerging trajectories, connecting them to computing literature and practice, and the broader context of connected and adjacent legal regimes, in particular copyright and relevant emerging case law. We find that there are a significant number of inclarities, tensions and contradictions both within the text, between the text and other legal regimes, and between the text and guideline documents, such as the Code of Practice on General-Purpose AI and recent guidelines by the European Commission. We identify a range of issues with the scoping of the provisions which may undermine its policy goals and create loopholes for regulatory avoidance, such as those relating to non-commercial models, open-source models, and model finetuning along the value chain. We find that the Code of Practice contains significant omissions and misstatements, some of which may present a compliance risk for an entity choosing to rely on the Code. We do not consider the provisions on GPAI models which present a systemic risk, which are dealt with elsewhere in the volume which this work will form a part of.

Shahidullah et al. on Intelligent Fraud Detection: Applying Advanced Analytics and Cybersecurity Insights in U.S. Finance

Mohammad Shahidullah (International American U (IAU)) et al. have posted “Intelligent Fraud Detection: Applying Advanced Analytics and Cybersecurity Insights in U.S. Finance” (Journal of Posthumanism, volume 4, issue 3, 2024 [10.63332/joph.v4i3.3593]) on SSRN. Here is the abstract:

Fraud detection in financial transactions is a major and crucial problem that does not cease to exist, mainly because of the enormous imbalance in the datasets obtained and the very high requirement for an accurate distinction between legitimate and fraudulent activities. In the following study, we assess the performance of three common machine learning models: Logistic Regression, Random Forest, and Gradient Boosting, for the detection of fraud, using a real-data set of transactions (284807 of which only 0.173% are labelled as fraudulent). The models were thoroughly evaluated with respect to critical metrics of performance including the precision, recall, F1-score and Area Under the Receiver Operating Characteristic Curve (AUC) to try to understand which of the models may be appropriate for dealing with class imbalance and false positives. Of the analyzed models, Random Forest was the best, with AUC being equal to 0.98, being superior to Logistic Regression (AUC = 0.97) and equal to Gradient Boosting (AUC = 0.98), while enabling more superior recall (0.88) and precision (0.44). This implies a higher capacity of detecting fraud cases without compromising the rate of false alarm too much. Feature importance analysis further noted that V14, V10, and V4 features were most predictive and most responsible for model classifying accuracy. Furthermore, calibration analysis revealed that Random Forest was the most reliable in estimating probabilities, outputs closely conformed to the ideal calibration curve implicating better reliability in practical applications. These findings suggest the effectiveness of the ensemble machine learning models especially Random Forest in promoting the efficacy of fraud detection systems. The study supports future research on real-time deployment and integration with deep learning methods to enhance the strength of fraud detection in the constantly changing financial spaces.

Schwarcz on Distributing Risk in an Age of AI: Procedural Bad Faith and AI Claims Handling

Daniel Schwarcz (U Minnesota Law) has posted “Distributing Risk in an Age of AI: Procedural Bad Faith and AI Claims Handling” on SSRN. Here is the abstract:

Written in honor of Kenneth Abraham and his foundational contributions to insurance law, this Essay argues that the rise of AI-driven insurance claims handling exposes a significant gap in first-party bad faith law. Traditional bad faith doctrine has focused primarily on outcomes, asking whether an insurer wrongfully denied or delayed payment of benefits owed under the policy. But increasingly automated claims processes create a distinct procedural injury when insurers deny, reduce, or delay claims without meaningful human review, adequate explanation, or a genuine opportunity for the insured to be heard. Drawing on procedural justice theory, the Essay shows that such practices can undermine voice, dignity, neutrality, and trustworthiness in a relationship defined by vulnerability and dependence. It therefore argues that courts should give procedural fairness substantially greater weight within the bad faith inquiry and should treat heavily automated claims denials without meaningful human oversight as powerful evidence of procedural bad faith. Doing so would adapt bad faith law to the distinctive risks posed by AI while preserving insurance’s core promise of fair, respectful, and accountable claims resolution.

Pollanen on The Probabilistic Foundations of Surveillance Failure: From False Alerts to Structural Bias

Marco Pollanen (Trent U) has posted “The Probabilistic Foundations of Surveillance Failure: From False Alerts to Structural Bias” on SSRN. Here is the abstract:

For decades, forensic statisticians have debated whether searching large DNA databases undermines the evidential value of a match. Modern surveillance faces an exponentially harder problem: screening populations across thousands of attributes using threshold rules rather than exact matching. Intuition suggests that requiring many coincidental matches should make false alerts astronomically unlikely. This intuition fails. Consider a system that monitors 1,000 attributes, each with a 0.5 percent innocent match rate. Matching 15 pre-specified attributes has probability 10^(-35), one in 30 decillion, effectively impossible. But operational systems require no such specificity. They might flag anyone who matches any 15 of the 1,000. In a city of one million innocent people, this produces about 226 false alerts. A seemingly impossible event becomes all but guaranteed. This is not an implementation flaw but a mathematical consequence of high-dimensional screening. We identify fundamental probabilistic limits on screening reliability. Systems undergo sharp transitions from reliable to unreliable with small increases in data scale, a fragility worsened by data growth and correlations. As data accumulate and correlation collapses effective dimensionality, systems enter regimes where alerts lose evidential value even when individual coincidences remain vanishingly rare. This framework reframes the DNA database controversy as a shift between operational regimes. Unequal surveillance exposures magnify failure, making “structural bias” mathematically inevitable. These limits are structural: beyond a critical scale, failure cannot be prevented through threshold adjustment or algorithmic refinement.