Studying People to Study AI: Expert Perspectives on the Epistemic Fit and Barriers of Human Research in AI Safety & Ethics

A recent study, based on expert surveys and interviews, highlights a significant disconnect in Artificial Intelligence Safety & Ethics (AISE) research. While there is broad consensus among experts regarding the value of human-centric research for assessing AI risks, its practical integration is hampered by perceived validity issues, resource constraints, and epistemological challenges. This suggests a systemic undervaluing of empirical human research in favor of more technical evaluation methods, despite the increasing evidence of AI risks in human interactions.

This analysis reveals a critical gap in how AI risks are assessed, potentially leading to incomplete or inaccurate understanding of real-world impacts. For senior executives, addressing this requires re-evaluating current risk assessment methodologies to ensure a comprehensive, human-centered approach, thereby safeguarding organizational reputation and operational resilience in an AI-driven landscape.

AI safety risks are increasingly manifest in human interactions with AI technologies.

Forward consideration, not a verified fact.