Real-World Gaps in AI Governance Research
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.
681594746a80babbe28775ba
Ilan Strauss
claimed_verified
2025-05-04T10:04:25.496Z
66225f7100352aeea584d02a
/avatars/ca13f59bebf73d03a63a935f628aea5c.svg
Ilan Strauss
user
strauss-NYC
681594746a80babbe28775bb
Isobel Moure
admin_assigned
2025-05-05T07:35:12.655Z
67535114d2a628475a0e7a6e
https://cdn-avatars.huggingface.co/v1/production/uploads/67535114d2a628475a0e7a6e/YSkBnVV393Q4-yIk2-U2-.jpeg
Isobel Moure
user
isobelmoure
681594746a80babbe28775bc
Tim O'Reilly
681594746a80babbe28775bd
Sruly Rosenblat
admin_assigned
2025-05-05T07:35:24.305Z
64582e94f8bdc3512d1ee940
/avatars/e0eb72f06c7da58cb569198540484ab1.svg
Sruly Rosenblat
user
sruly
Real-World Gaps in AI Governance Research
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.
Research by leading AI organizations focuses more on pre-deployment stages like model alignment and testing over deployment issues such as bias, with significant gaps existing in high-risk areas like healthcare and finance.
model alignment testing & evaluation model bias high-risk deployment domains healthcare finance misinformation persuasive features addictive features hallucinations copyright observability in-market AI behaviors