Closing the AI Knowledge Gap

AI systems are growing faster than our ability to study them: The paper identifies a critical problem where new AI systems are being created and deployed at a pace that far outstrips the number of rigorous scientific studies examining how these systems actually behave in practice.

Current AI research focuses too narrowly on engineering performance: AI researchers typically prioritize whether systems work well for their intended technical purpose, but this leaves important questions about fairness, bias, and broader societal impacts largely unstudied—a gap that requires different types of expertise to address.

Social and behavioral scientists are underutilized in AI research: The paper argues that researchers trained in psychology, sociology, and related fields are well-positioned to test hypotheses about AI behavior but face significant barriers to participating in this work, meaning valuable perspectives are missing from the conversation.

Structural barriers in academia and industry prevent broader scientific study of AI: Current incentive systems, lack of access to AI systems, and absence of appropriate tools make it difficult for researchers outside computer science to conduct rigorous studies of how AI systems behave.

A marketplace model could democratize AI research: The proposed "TuringBox" platform would allow diverse researchers to access AI systems for scientific study and contribute their own evaluation methods, potentially accelerating our understanding of AI behavior across different domains and use cases.