現實世界中AI治理研究的缺口

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

基於從9,439篇生成式AI論文中篩選出的1,178篇安全與可靠性研究（時間跨度為2020年1月至2025年3月），我們比較了領先AI企業（Anthropic、Google DeepMind、Meta、Microsoft和OpenAI）與頂尖AI學術機構（卡內基梅隆大學、麻省理工學院、紐約大學、史丹佛大學、加州大學柏克萊分校和華盛頓大學）的研究產出。我們發現，企業AI研究日益聚焦於部署前階段——模型對齊及測試與評估——而對部署階段問題如模型偏見的關注有所減弱。在高風險部署領域，包括醫療保健、金融、虛假信息、說服性與成癮性功能、幻覺問題及版權方面，存在顯著的研究空白。若無法提升對已部署AI的可觀測性，企業研究的集中化趨勢可能加劇知識赤字。我們建議擴大外部研究人員對部署數據的訪問權限，並系統性地觀測市場中AI的行為表現。

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.