Real-World Gaps in AI Governance Research
This highlights critical gaps in AI governance research that could undermine safety and reliability in real-world applications, particularly as corporate concentration grows.
The study analyzed 1,178 safety and reliability papers from 9,439 generative AI papers (2020-2025) to compare research outputs of leading AI companies and universities, finding that corporate research increasingly focuses on pre-deployment areas like model alignment while neglecting deployment-stage issues such as bias, with significant gaps in high-risk domains like healthcare and misinformation.
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.