AI liability
In plain wordsWho has to pay or answer in court when an AI system causes harm — the company that built it, the business that used it or the person who relied on it — and under which laws.
Legal responsibility for harm caused by AI systems, allocated among developers, deployers and users under doctrines such as negligence, product liability (design defect, failure to warn), misrepresentation, defamation, discrimination law and contract. AI strains these doctrines because systems behave probabilistically, act with some autonomy, are opaque, pass through long supply chains and may count as services rather than products. Early cases include Moffatt v. Air Canada (2024), in which a Canadian tribunal held an airline bound by its chatbot's misstatement about fares; Garcia v. Character Technologies, in which a US federal court in 2025 declined to dismiss wrongful-death claims over a companion chatbot, rejecting at that stage the argument that its output was protected speech; and suits filed against OpenAI from 2025 alleging that ChatGPT encouraged suicides. In the EU, the revised Product Liability Directive ((EU) 2024/2853) treats software, including AI, as a product from December 2026, while the proposed AI Liability Directive was withdrawn in 2025. Whether US Section 230 immunity covers generated output is unsettled.
The airline argued its chatbot was a separate legal entity; the tribunal rejected that view of AI liability.
Origin
Legal scholarship on liability for autonomous and expert systems dates to at least the 1980s; the debate intensified with self-driving cars in the 2010s and with generative AI after 2022. The European Commission proposed its AI Liability Directive in September 2022.
Usage note
Proposals range from strict liability for developers of frontier models for catastrophic harms to safe harbors for developers that follow recognized safety frameworks; the US debate is also entangled with AI preemptionAI preemption, n.: The push in the United States for a single national AI law that would override the states' own AI laws, and the fight over whether that is wise or even lawful..
Related
- algorithmic accountabilityalgorithmic accountability, n.: The idea that the people and organizations behind automated decision systems must be able to explain and justify those systems' effects, answer for the harm they cause, and fix it.
- AI auditAI audit, n.: An independent check of an AI system, or of the company using it, against agreed standards — for example, testing whether a hiring tool treats women and men fairly.
- SB 1047SB 1047, prop. n.: A 2024 California bill that would have required makers of the biggest AI models to adopt safety plans and take care to prevent catastrophes. It passed the legislature, but the governor vetoed it.
- AI whistleblower protectionsAI whistleblower protections, n.: Legal and company safeguards that protect employees of AI companies who report safety dangers or legal violations — to regulators or inside the company — from being fired, sued or otherwise punished.
- AI companionAI companion, n.: A chatbot designed to be a friend, partner or confidant rather than a tool: it remembers you, has a personality and keeps up an ongoing relationship.
- hallucinationhallucination, n.: When an AI states something false or made up — a fake fact, quotation or source — fluently and confidently, as if it were true.
Mentioned in
AI regulationAI regulation, n.: Laws and binding government rules about artificial intelligence: what AI may be used for, what checks its makers must run, and what penalties apply when they break the rules., AI incidentAI incident, n.: A real-world case in which an AI system, through a flaw, a breakdown or the way someone used it, ends up hurting people, property or rights, as in an injury, a wrongful arrest or a privacy breach., AI personhoodAI personhood, n.: The idea of treating an AI system as a 'person' in law or ethics, with rights or duties of its own — for example, to own property, to be sued, or not to be deleted., AI whistleblower protectionsAI whistleblower protections, n.: Legal and company safeguards that protect employees of AI companies who report safety dangers or legal violations — to regulators or inside the company — from being fired, sued or otherwise punished., algorithmic accountabilityalgorithmic accountability, n.: The idea that the people and organizations behind automated decision systems must be able to explain and justify those systems' effects, answer for the harm they cause, and fix it., responsibility gapresponsibility gap, n.: The problem that when an AI system that learns or acts on its own causes harm, it may be unclear whether anyone (its makers, users or operators) can fairly be held responsible., SB 1047SB 1047, prop. n.: A 2024 California bill that would have required makers of the biggest AI models to adopt safety plans and take care to prevent catastrophes. It passed the legislature, but the governor vetoed it., Section 230Section 230, prop. n.: A 1996 US law that generally protects websites and apps from being sued over what their users post, and lets them remove content in good faith. Whether it protects what AI chatbots themselves say is unsettled., moral crumple zonemoral crumple zone, n.: When a largely automated system goes wrong and the human who was supposed to supervise it gets blamed, even though they had little real say; named after the car part that soaks up a crash., Product Liability DirectiveProduct Liability Directive, prop. n.: The EU law that lets people claim compensation, without proving anyone was careless, when a defective product injures them — rewritten in 2024 so that software and AI count as products., SB 243SB 243, prop. n.: A 2025 California law requiring AI companion chatbots to tell users they are not human, respond safely when people talk about suicide, and protect minors, including with regular reminders to take a break.