企业研究偏重上线前，部署后风险被忽视
Real-World Gaps in AI Governance Research
- 对比9439篇生成式AI论文，发现企业聚焦模型对齐与测试
- 医疗、金融等高风险领域部署问题研究严重不足
- 建议开放部署数据，建立真实场景下的可观测机制
基于2020年1月至2025年3月间收录的9,439篇生成式AI论文中的1,178篇安全与可靠性研究，我们比较了Anthropic、Google DeepMind、Meta、Microsoft、OpenAI等领先企业，以及CMU、MIT、NYU、Stanford、UC Berkeley、华盛顿大学等高校的研究产出。结果表明，企业研究日益集中于模型对齐和测试评估等上线前环节，而对上线后如模型偏见、医疗、金融、虚假信息、诱导性与成瘾功能、幻觉及版权等高风险部署问题的关注明显减弱。在缺乏对已部署AI系统有效观测的情况下，企业主导的研究格局可能加剧知识盲区。为此，我们建议扩大外部研究人员对部署数据的访问权限，并建立对市场中AI行为的系统性可观测机制。
原文摘要 · Abstract (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.
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