Gabriel Weil

Epistemic status: This is a summary of an 80-page law review article that took me ~6 months to research and write. I am highly confident about my characterization of existing U.S. tort law and its limits. This characterization is only contested by other legal scholars and analysts on the margin. My prescriptions are much more controversial, though most of the objections come from people who are skeptical of AI catastrophic risk and/or the political viability of my proposals, rather than the merits (on the assumption that advanced AI systems do generate catastrophic risks). TLDR: Legal liability could substantially mitigate AI risk, but current law falls short in two key ways: (1) it requires provable negligence, and (2) it greatly limits the availability of punitive damages. Applying strict liability (a form of liability that does not require provable negligence) and expanding the availability and flexibility of punitive damages is feasible, but will require action by courts or legislatures. Legislatures should also consider acting in advance to create a clear ex ante expectation of liability and imposing liability insurance requirements for the training and deployment of advanced AI systems. The following post is a summary of a law review article. Here is the full draft paper. Dylan Matthews also did an excellent write-up of the core proposal for Vox’s Future Perfect vertical. AI alignment is primarily a technical problem that will require technical solutions. But it is also a policy problem. Training and deploying advanced AI systems whose properties are difficult to control or predict generates risks of harm to third parties. In economists’ parlance, these risks are negative externalities and constitute a market failure. Absent a policy response, products and services that generate such negative externalities tend to be overproduced. In theory, tort liability should work pretty well to internalize these externalities, by forcing the companies that train and d