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.