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”（在包括互联网在内的海量信息上训练的大型模型），能够生成在多种责任制度下可能引发问题的言论。机器学习从业者通常会进行“红队测试”，以识别并减轻此类问题言论——从诬告他人严重不当行为的“幻觉”到制造原子弹的配方等。关键问题在于：这些被红队测试出的行为是否依据美国法律对模型创建者和部署者构成实际的责任风险，从而激励对安全机制的投资？我们考察了三种责任制度，并将其与常见的红队测试模型行为示例相关联：诽谤、犯罪行为的构成性言论以及过失致人死亡。我们发现，任何第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。而要使模型（及其关联方）真正对生成的言论承担责任，存在诸多障碍。我们认为，在这些场景中，AI不应被一概免除责任；随着法院已经着手应对平台算法中的精细复杂性，生成式AI技术细节所引发的更为棘手的难题正日益凸显。法院和政策制定者在评估这些问题时，应当审慎考量他们将创造怎样的技术设计激励机制。