title: Real-World Gaps in AI Governance Research
summary: 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.
paper_authors._id: 681594746a80babbe28775ba
paper_authors.name: Ilan Strauss
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-04T10:04:25.496Z
paper_authors._id: 66225f7100352aeea584d02a
paper_authors.avatarUrl: /avatars/ca13f59bebf73d03a63a935f628aea5c.svg
paper_authors.fullname: Ilan Strauss
paper_authors.type: user
paper_authors.user: strauss-NYC
paper_authors._id: 681594746a80babbe28775bb
paper_authors.name: Isobel Moure
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:35:12.655Z
paper_authors._id: 67535114d2a628475a0e7a6e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/67535114d2a628475a0e7a6e/YSkBnVV393Q4-yIk2-U2-.jpeg
paper_authors.fullname: Isobel Moure
paper_authors.type: user
paper_authors.user: isobelmoure
paper_authors._id: 681594746a80babbe28775bc
paper_authors.name: Tim O'Reilly
paper_authors._id: 681594746a80babbe28775bd
paper_authors.name: Sruly Rosenblat
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:35:24.305Z
paper_authors._id: 64582e94f8bdc3512d1ee940
paper_authors.avatarUrl: /avatars/e0eb72f06c7da58cb569198540484ab1.svg
paper_authors.fullname: Sruly Rosenblat
paper_authors.type: user
paper_authors.user: sruly
paper_title: Real-World Gaps in AI Governance Research
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: model alignment testing & evaluation model bias high-risk deployment domains healthcare finance misinformation persuasive features addictive features hallucinations copyright observability in-market AI behaviors