现实世界中AI治理研究的空白领域

Real-World Gaps in AI Governance Research

April 30, 2025

作者: Ilan Strauss, Isobel Moure, Tim O'Reilly, Sruly Rosenblat

cs.AI

摘要

基于从9,439篇生成式AI论文（2020年1月至2025年3月）中筛选出的1,178篇安全性与可靠性研究，我们对领先的AI企业（Anthropic、Google DeepMind、Meta、微软及OpenAI）与顶尖AI学术机构（卡内基梅隆大学、麻省理工学院、纽约大学、斯坦福大学、加州大学伯克利分校及华盛顿大学）的研究成果进行了对比分析。研究发现，企业AI研究日益聚焦于部署前阶段——模型对齐及测试与评估——而对部署阶段问题如模型偏见的关注有所减弱。在高风险部署领域，包括医疗健康、金融、虚假信息、诱导性与成瘾性功能、幻觉现象及版权问题等方面，存在显著的研究空白。若不对已部署AI的可观测性加以提升，企业研究集中度的加剧可能进一步扩大知识鸿沟。为此，我们建议扩大外部研究人员对部署数据的访问权限，并系统性地增强对市场内AI行为的可观测性。

English

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.