Generative AI, in particular text-based "foundation models" (large models trained on a huge variety of information including the internet), can generate speech that could be problematic under a wide range of liability regimes. Machine learning practitioners regularly "red team" models to identify and mitigate such problematic speech: from "hallucinations" falsely accusing people of serious misconduct to recipes for constructing an atomic bomb. A key question is whether these red-teamed behaviors actually present any liability risk for model creators and deployers under U.S. law, incentivizing investments in safety mechanisms. We examine three liability regimes, tying them to common examples of red-teamed model behaviors: defamation, speech integral to criminal conduct, and wrongful death. We find that any Section 230 immunity analysis or downstream liability analysis is intimately wrapped up in the technical details of algorithm design. And there are many roadblocks to truly finding models (and their associated parties) liable for generated speech. We argue that AI should not be categorically immune from liability in these scenarios and that as courts grapple with the already fine-grained complexities of platform algorithms, the technical details of generative AI loom above with thornier questions. Courts and policymakers should think carefully about what technical design incentives they create as they evaluate these issues.

翻译：生成式AI，尤其是基于文本的“基础模型”（在大规模多样化数据（包括互联网）上训练的大型模型），可能生成在多种责任制度下存在问题的话语。机器学习从业者经常对模型进行“红队测试”，以识别并缓解此类问题言论：从错误指控他人严重不当行为的“幻觉”，到制造原子弹的配方。一个关键问题是，这些经红队测试的行为是否真正构成模型创建者和部署者在美国法律下的责任风险，从而激励对安全机制的投资。我们考察了三种责任制度，并将其与红队测试模型行为的常见实例联系起来：诽谤、构成犯罪行为组成部分的言论以及过失致死。我们发现，任何《美国通信规范法》第230条豁免分析或下游责任分析，都与算法设计的技术细节紧密交织。真正追究模型（及其相关方）对生成言论的责任存在诸多障碍。我们认为，AI在这些情形下不应被一概免除责任，而随着法院已着手处理平台算法中错综复杂的细节，生成式AI的技术细节将引发更加棘手的难题。法院和政策制定者在评估这些问题时，应审慎考虑其设立了何种技术设计激励。