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.

Huijuan Peng (Singapore Management U Yong Pung How Law) and Pey-woan Lee (Singapore Management U Yong Pung How Law) have posted “Reimagining U.S. Tort Law for Deepfake Harms: Comparative Insights from China and Singapore” (Journal of Tort Law, 0[10.1515/jtl-2025-0028]) on SSRN. Here is the abstract:

This Article explores how U.S. tort law can respond more effectively to the distinct harms posed by deepfakes, including reputational injury, identity appropriation, and emotional distress. Traditional tort doctrines, such as defamation, the right of publicity, and intentional infliction of emotional distress (IIED), remain fragmented and ill-suited to the speed, scale, and anonymity of deepfake dissemination. Using a comparative functionalist approach, the Article analyzes how China and Singapore respond to deepfake harms through structurally divergent but functionally instructive frameworks. China’s model combines codified personality rights with intermediary obligations under a civil law regime, while Singapore adopts a hybrid approach that integrates common law torts with targeted statutory and administrative interventions. Although neither model is directly replicable in the United States, both offer valuable comparative insights to guide the reform of U.S. tort law. The article advances an integrated governance model for U.S. tort law: reconstructing personality-based torts, repositioning tort law through conditional intermediary liability, and clarifying constitutionally grounded limits for speechbased claims. Drawing on Chinese and Singaporean legal approaches, the Article sets out a comparative reform framework that enables U.S. tort law to better address deepfake harms while safeguarding autonomy and dignity in AI-driven digital environments.

Bryant Walker Smith (U South Carolina Joseph F. Rice Law) has posted “”Self-Driving” Means Self-Driving” (Forthcoming in Drake Law Review) on SSRN. Here is the abstract:

Tesla uses the name “Full Self-Driving” to market a driver assistance system that still requires its user to pay attention to the road. And yet, as this article documents, there is a broad consensus among developers and regulators of motor vehicle technologies, including Tesla itself, that the term “self-driving” correctly refers only to a system whose user does not need to pay attention. This conclusion is foundational to multiple ongoing legal proceedings around the world.

Trent Kannegieter (Yale U Law) has posted “Nondeterministic Torts: A Mechanistic Approach to Large Language Model Tort Liability” on SSRN. Here is the abstract:

Our laws were built for deterministic machines. When given the same inputs, we expect a system to consistently produce the same outputs. But modern artificial intelligence (AI) systems, specifically large language models (LLMs) and other types of “generative AI” (GenAI), challenge this assumption. These systems are nondeterministic, meaning that they produce varied outputs even when given identical inputs. By their nature, nondeterministic LLM-based predictions carry an arbitrary randomness that is an inherent feature, not a bug, of the product.

Even as AI applications spread rapidly, liability for AI systems going wrong remains an open question. But despite the rise of attention and scholarship around artificial intelligence since the “GenAI explosion” sparked by ChatGPT’s release in fall 2022, researchers have so far overlooked nondeterminism’s profound consequences for the law of AI.

This article argues that nondeterminism is the key link to successfully arguing a host of tort claims against the creators and deployers of AI products when these products cause injury. The inherent dangers of deploying a nondeterministic, LLM-based system give rise to multiple potential tort claims, especially when AI systems are deployed in contexts that aresafety-critical (where the cost of individual errors is comparatively high) oragentic (when AI agents have comparatively few checks from humans or deterministic software). Even if the average legal reader isn’t aware of nondeterminism, the average AI engineer is. By deploying a system that is known to be nondeterministic and unpredictable, developers might be accepting responsibility for the harms that emerge from a model’s behavior.

Nondeterminism is the critical hook into multiple popular tort doctrines, especially negligence and product liability (design defect) claims. In claims of negligence, developer knowledge of nondeterminism helps establish a duty and makes even nominally unexpected harms foreseeable, as they are downstream from an unpredictable, nondeterministic system. In product liability, nondeterministic systems deployed in safety-critical or agentic contexts might be a defective design for their use case. Quality assurance (QA) procedures, critical to ensuring the safety of mission-critical systems, are inherently incomplete for nondeterministic systems. Nondeterminism might even make deploying an LLM-based system in such contexts an abnormally dangerous activity, further strengthening the case for a strict liability regime.

This piece arrives at a critical moment in the development of common law around AI. AI might be eating the world, but regulation has not kept pace. Little federal legislative movement is expected, leaving soft law (industry self-regulation) as a stopgap. But the rewards for AI companies deploying quickly in an era of skyrocketing valuations will likely prove too large to ignore. Without a clear liability regime, firms see little costs to counter the lucrative benefits of capturing the AI market. Absent additional regulation, the common law as it stands today is the best tool in today’s toolkit to guard against AI harms.

The article offers a novel, mechanistic approach to assessing AI liability in high-stakes domains: beginning with technical and use case specificity. By looking at the technical features of products on the market today, we can discover ways to use existing doctrine to regulate new technologies. Inquiries into the law of AI must begin with how AI systems work. This framework could drive meaningful reforms, helping strengthen the effort of using tort law to guard against AI harms without having to rely on speculative future harms. If utilized, the arguments within this piece might incentivize AI product developers—the least cost avoiders—to only deploy agentic GenAI systems when the benefits exceed the costs.

Beatriz Botero Arcila (Institut d’Etudes Politiques Paris (Sciences Po) Sciences Po Law Ecole Droit Sciences Po) has posted “AI Liability Along the Value Chain” (Published by Mozilla Foundation) on SSRN. Here is the abstract:

Policymakers around the world are increasingly preoccupied with identifying mechanisms to better assign accountability and liability throughout the AI value chain. Particularly in the EU, discussions around civil liability and AI received significant attention after the proposal of an AI Liability Directive (AILD) in 2022. While this proposal was recently withdrawn by the European Commission, the challenges posed by AI for civil liability and harmed individuals’ ability to seek redress remain more relevant than ever amid increasing adoption of AI across sectors. 

This report thus seeks to provide more conceptual clarity to these challenges and provide recommendations on what an effective AI liability framework could look like. Though it is common to think of AI systems as a singular tool, AI systems are often developed and deployed in a value chain that involves numerous actors that participate throughout the stages of creation, fine-tuning, and implementation of these technologies, or that sell and supply key components such as pre-labeled data. 

When designing a liability system for this type of multi-party scenario, there are many questions to consider: should all parties in the value chain be held equally liable when harm occurs? Or should each actor only be held liable for the extent to which they are responsible? How easy is it to establish the contribution of each party? (Spoiler alert, it may be very hard.) Another question lawyers will be familiar with is what is the right standard — should AI actors be held liable only when they fail to take the right safety measures? Or should they be held liable regardless of whether they took safety measures, simply because by developing or deploying an AI system or model they created a risk? 

This Report discusses these questions and the complexities of assigning liability along the AI value chain, given the involvement of multiple actors in the design, development, and deployment of AI systems. The Report explores various configurations of AI value chains, the roles of different actors, and how companies allocate liability amongst them via contracts and terms. It then examines different policy choices for designing liability regimes.