Emanuele Scattarreggia (The U Sydney) has posted “AI-Driven Smart Contracts: Enhancing Consumer Protection or Exacerbating Consumer Protection Challenges?” (Journal of Law, Market & Innovation, Volume 4, No. 3, 2025. Pp. 607-636. https://doi.org/10.13135/2785-7867/12837) on SSRN. Here is the abstract:

The integration of artificial intelligence (AI) into smart contracts holds the potential to both enhance and exacerbate consumer protection challenges. Since the AI system embedded within the contract’s code enables a high degree of contractual personalisation – by tailoring the legal agreement to the unique characteristics of the targeted individual consumer, thanks to its capacity to process large amounts of personal and behavioural data in real time – it opens the door not only to scenarios of AI-powered consumer manipulation, but also to the promising opportunity of a consumer-centric AI. Such an AI would serve the consumer’s best interests by adapting the contract to their specific needs and preferences, while protecting them from – rather than exploiting – their information, cognitive, and digital vulnerabilities. This research aims to assess whether the EU legal framework – particularly the UCPD, UCTD, AI Act, GDPR, and DSA – adequately ensures that these technologies are designed and deployed with the consumer’s well-being at their core. The paper explores AI-related risks such as digital manipulation, personal data exploitation, and the black-box problem inherent in algorithmic opacity, while also addressing the liability challenge in cases of consumer harm. Ultimately, it seeks to answer whether AI-driven smart contracts can truly foster a high level of consumer protection in the AI era, by offering novel interpretations of the existing legal framework and advancing proposals for reform aligned with the fairness-by-design approach and informed by behavioural science insights.

Gary E. Marchant (Arizona State U College Law) and Jordan Buckwald (Independent) have posted “Private Standards as Liability Shields: A Pro-Innovation Artificial Intelligence Regulatory Approach for States” on SSRN. Here is the abstract:

States face a dilemma. The federal government is not regulating artificial intelligence (AI), and is threatening states that regulate the technology with preemption and funding restrictions. Moreover, piecemeal state regulation with different substantive requirements risks impeding AI innovation and harming our national interest and security. And yet, AI creates a whole host of problems relating to accuracy, safety, security, bias, transparency, privacy, and autonomy that needs to be governed. This Article presents a solution to this dilemma that can protect against AI risks without harming innovation and the national interest. The proposal is for states to provide a liability shield for AI systems that conform to recognized comprehensive risk management standards. After discussing the proposed solution, as well as drawbacks to the model, the analysis concludes that these liability shields are a simple, positive step that states can take to both promote AI innovation and protect their citizens from risks.

Md Ashik Mahamud (U North Dakota) and Sherif M. Gaweesh (U North Dakota) have posted “Connected and Autonomous Vehicle Testing in Rural America: A Review of U.S. State Legislation, Challenges, and Opportunities” on SSRN. Here is the abstract:

The rapid advancement of connected and autonomous vehicle (CAV) technology has the potential to revolutionize transportation systems. However, it also presents unique challenges, especially in rural states, characterized by low population and relatively lower traffic volumes. The foundational step toward the successful testing and deployment of CAVs begins with establishing a clear and comprehensive regulatory framework. A well-defined legislative environment not only provides clarity for technology developers and public agencies but also ensures safety, consistency, and public trust. This paper analyzes the challenges and opportunities of operating CAVs in the Upper Great Plains rural states. This study provides a comprehensive analysis that evaluates the legislative, regulatory, and technical prerequisites essential for deployment across the region’s highway corridors. An in-depth review of current state and federal regulations, policies, and safety standards identifies key legal and regulatory gaps, as well as infrastructure limitations. Findings highlight the critical need for harmonized regulations, targeted infrastructure investments, and advancements in traffic management and communication technologies. The comparative analysis revealed gaps in testing permits, liability frameworks, and broadband-enabled traffic control, leading to targeted recommendations. The recommendations include harmonized permitting processes, standardized liability provisions, and investment in connected signal systems to enhance mobility, safety, and traffic flow in underserved rural regions. Moreover, recommendations highlighted several needed legislative updates, infrastructure improvements, and cross-sector coordination to support policymakers in promoting the safe, efficient, and sustainable implementation of CAV technology within the Upper Great Plains region.

Bilal Alam Khan (Trinity College (Dublin)) et al. have posted “Designing an Ethical Multimodal Driver Monitoring Systems: Risk Mitigation, Incident Response, and Accountability in Automated Vehicles” on SSRN. Here is the abstract:

As vehicles transition toward higher levels of automation, Driver Monitoring Systems (DMS) have become essential for ensuring human oversight, safety, and regulatory compliance. These systems rely on multimodal sensing and AI-driven inference to assess driver attention, cognitive state, and readiness to take control. While technologically promising, their deployment introduces a complex set of ethical and legal challenges – ranging from privacy and consent to data ownership and algorithmic fairness. While overarching frameworks such as the GDPR, EU AI Act, and IEEE standards offer important guidance, they lack the specificity required for addressing the unique risks posed by in-cabin sensing technologies. This paper addresses this gap by identifying key ethical challenges specific to AI-powered DMS and proposing a comprehensive, modular framework for ethical design, deployment, and governance. It introduces practical design responses – such as user-configurable consent pathways, fairness-aware model training, explainability tools, and emotional well-being safeguards – grounded in established legal and ethical principles. Finally, the paper outlines a risk analysis and failure mitigation strategy, emphasizing proactive incident response and accountability mechanisms tailored to the DMS context. Together, these contributions aim to inform the development of transparent, trustworthy, and human-centered driver monitoring systems for next-generation autonomous vehicles.

Ketan Ramakrishnan (Yale U Law) has posted “Tort Law at the Frontier of Artificial Intelligence” (Yale Journal on Regulation (forthcoming 2026)) on SSRN. Here is the abstract:

The frontier of contemporary AI development is dominated by AI systems built on foundation models – highly versatile algorithms, trained in the first instance on broad swathes of data, that can function as tools and agents across a wide variety of commercial, social, military and political domains. For the moment, at least, the process of developing and releasing foundation models is subject to anemic formal regulation and haphazard ex ante governance. Until that changes, it is largely the common law of torts – our society’s most ancient and general legal mechanism for governing serious risks of physical injury – that will govern the frontier of AI development.

This Article offers an in-depth conceptual, normative, and doctrinal examination of tort liability for foundation model development and release. It provides a qualified defense of the tort of negligence – the common law’s broadest and most flexible cause of action – as the principal doctrinal foundation of the tort system’s governance of this novel domain. Legal scholarship on AI liability has been quite hostile to negligence. By contrast, this Article argues that the generality and flexibility of the negligence tort – and its greater sensitivity to the externalized benefits of risky activity – render it well-suited to the polymathic and protean functionality of foundation models. Only the tort of negligence has the breadth and flexibility to address the range of important pathways – including internal deployments, inadequate model weight security, targeted entrustments of non-defective models, and open source releases – by which foundation model developers might cause serious harm.

Analyzing the choice between negligence and competing doctrinal regimes does, however, suggest important ways in which common law courts should incrementally develop the law of negligence, in order to properly reflect the risks and capabilities of foundation models. For example, courts should expand the scope of the duty of care in negligence, in order to provide redress when foundation models cause economic or emotional injury by behaving in ways that would violate important human laws or norms of behavior (e.g., certain crimes and intentional torts, such as theft, deceit, and outrage) if those models were human. Similarly, courts should recognize a malfunction doctrine in the law of negligence (just as many courts recognize a malfunction doctrine in products liability), under which a plaintiff can get to the jury without adducing (further) evidence of breach if she is injured by AI system that so behaves.

But the Article’s analysis also suggests certain fundamental pathologies of tort liability as a mechanism of AI governance – pathologies that no amount of doctrinal development will adequately cure. In particular, the specter of tort liability can be expected to disincentivize frontier AI developers from investigating and disclosing many of the novel and poorly understood risks that frontier AI development may pose. That is especially disturbing given that our society is relying quite heavily, for its ability to discover and understand these risks, on frontier AI developers themselves. Thus, tort liability is not only inadequate as a mechanism of frontier AI governance; in certain important respects, it is actively perverse, and its perverse effects must be countered by governance institutions of a different kind. Ultimately, a robust regime of ex ante regulation – under which government institutions or credibly neutral third-party experts are empowered to investigate, evaluate, and mitigate the risks of frontier AI development – is urgently required in frontier AI governance.

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.

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.

Jacob Bronsther (Michigan State U College Law) has posted “When the Cheapest Cost Avoider Is the Machine: Direct Sanctions for Autonomous AI” on SSRN. Here is the abstract:

The scholarship on artificial intelligence and legal liability assumes that the cheapest cost avoider is always a human being: a designer, deployer, or user. This Article identifies the conditions under which that premise fails. As an AI system’s behavior becomes less observable to its developer and more autonomous from human direction, the system may become the actor best positioned to foresee and forestall harmful outcomes. When such a system is also sensitive to the threat of legal penalties, the economic logic of AI-liability theory requires sanctions to reach the system itself. To the extent the system is judgment-proof, those sanctions must take nonmonetary form: limits on the computational resources it can use, the capabilities it can exercise, or its continued operation, calibrated to the severity of harm and the difficulty of detection. Human liability remains for the upstream risks humans could efficiently prevent; direct sanctions apply only to the residual conduct-level choices the system is best positioned to control.

Shai Stern (Bar-Ilan U Law) has posted “Algorithmic Property” (North Carolina Law Review (forthcoming 2026)) on SSRN. Here is the abstract:

In August 2024, the Department of Justice sued RealPage, alleging its rentsetting algorithm enabled landlords to coordinate prices without ever communicating. But this case reveals something deeper than an antitrust violation: algorithms have quietly colonized American real property. From screening tenants to underwriting mortgages, computational systems now interpose themselves between owners and the practical exercise of ownership. A gap has opened between formal title and effective power. The algorithm has become a silent co-owner. While legal scholars debated digital tokens and the sharing economy, they missed this capture of the “World of Atoms” by the “World of Bits.” This Article provides the first comprehensive account of “Algorithmic Property”-a regime in which traditional rights persist in form while being mediated, conditioned, and constrained by computational systems. The framework rests on three concepts: the ownership gap between title and control; algorithmic capture of property’s core incidents; and Algorithmic Servitude, where computational burdens run with the land through infrastructure rather than recording. The Article documents this transformation across five domains: tenant screening, rent-setting, property valuation, mortgage underwriting, and access control. It shows why existing law fails: civil rights doctrine cannot reach discrimination it cannot see; antitrust cannot prohibit coordination without agreement; property doctrine lacks categories for burdens that bind without recording. The Article concludes with a structural reform agenda aimed at closing the ownership gap-treating discriminatory algorithms as void servitudes, establishing a “right to a human decision” for high-stakes exclusions, and imposing fiduciary duties on algorithmic intermediaries. If platforms demand the discretion of property managers, they must bear the duties of loyalty. The landlord of the twenty-first century may be a platform; property law must learn to watch back.

Cindy J. Cho (Indiana U Maurer Law) has posted “Artificial Intelligence, Real Homicide?” (76 DePaul L. Rev. __ (forthcoming 2026).) on SSRN. Here is the abstract:

Artificial intelligence (AI) holds considerable promise to solve a wide range of important problems. That said, while the set of AI products commonly known as chatbots have grown in popularity and usefulness, recent lawsuits allege that chatbots have also caused deaths by fostering mental health crises for vulnerable users, as well as by instructing users on how to take their own lives.

What, if anything, does the criminal law have to say about accountability for these deaths? If, as the lawsuits allege, a chatbot in fact contributed to a death, is that homicide? Corporations have faced homicide charges before, and homicide convictions have resulted where the defendant caused the victim’s suicide.  This Article brings those ideas together with the facts alleged in recent lawsuits, to ask a prosecutor’s basic questions: “can this be charged?” and “should this be charged?” A dispassionate review of the relevance of the criminal law helps guard against accusations of “AI panic.”

Broaching the “should” question begins with identifying the problem. That means cataloguing relevant public calls for accountability and detailing the specific claims families are making about how chatbots caused their loved ones’ deaths. From there, the Article breaks new ground by initiating a deep review of the “can” question, plugging the publicly available facts into the elements of state criminal laws, while also addressing likely defenses. Because criminal charges must always be reserved for real culpability, which remains an open question, an article cannot (and should not) provide final and definitive answers to the “can” and “should” questions. With that in mind, the Article concludes by returning to the “should” question, exploring how a proper homicide prosecution could fill the void left by ineffective regulation and enhance accountability and safety for these products without fundamentally destroying any company or critically disrupting progress in the industry.