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.

基于大量信息训练的生成式人工智能（特别是以文本为基础的 “基本模型”）在产生问题性言论方面可能面临不同责任体系的风险。因此需要对这些模型进行 “红队测试”，以识别和缓解潜在的问题性言论。本研究考察了三种责任体系，并将其与普遍的红队测试模型行为进行关联：诽谤、涉及犯罪行为的言论以及错误死亡。研究发现，对于生成式言论模型的 Section 230 免责分析或下游责任分析密切关联于算法设计的技术细节。文章主张在这些情况下 AI 不应被绝对地免除责任。法院和决策者在评估这些问题时应慎重考虑所造成的技术设计激励措施并需同时应对平台算法的复杂性。