  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.
