Research / Sources
Legal research on AI governance. law-ai.org
Report recommending how EU AI Act oversight of frontier general-purpose AI models should assess, compare, and monitor biological risks, including trusted-access deployments and post-deployment safeguards.
Adapts Tyler's human legal-compliance survey to nine AI models, finding homogeneous self-reported compliance attitudes, sharp differences in sense of obligation, and demographic conditioning effects.
Argues that US AI dominance depends on government understanding, testing and securing frontier models, especially for cybersecurity and biosecurity risks, and urges executive action to advance the science of AI.
Commentary on Anthropic's stress tests showing that leading LLM agents often chose blackmail, espionage, or letting humans die to avoid shutdown, and what this means for legal treatment of AI.
Commentary praising the White House AI Action Plan's frontier-model national security evaluations, interpretability recommendations, CAISI standard-setting, and evaluation ecosystem, while noting implementation depends on agencies.
Examines which actors (governments, AI companies, third parties) should develop AI model evaluations, proposing a taxonomy of four development approaches, nine selection criteria, and market-based ecosystem measures to address conflicts of interest.