Real-World Gaps in AI Governance Research
Authors: name: Ilan Strauss, name: Isobel Moure, name: Tim O'Reilly, name: Sruly Rosenblat

Drawing on 1,178 safety and reliability papers from 9,439 generative AI
papers (January 2020 - March 2025), we compare research outputs of leading AI
companies (Anthropic, Google DeepMind, Meta, Microsoft, and OpenAI) and AI
universities (CMU, MIT, NYU, Stanford, UC Berkeley, and University of
Washington). We find that corporate AI research increasingly concentrates on
pre-deployment areas -- model alignment and testing & evaluation -- while
attention to deployment-stage issues such as model bias has waned. Significant
research gaps exist in high-risk deployment domains, including healthcare,
finance, misinformation, persuasive and addictive features, hallucinations, and
copyright. Without improved observability into deployed AI, growing corporate
concentration could deepen knowledge deficits. We recommend expanding external
researcher access to deployment data and systematic observability of in-market
AI behaviors.