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This study investigates the barriers to integrating human research into AI safety and ethics by surveying 93 experts and conducting interviews with 17 AISE researchers from diverse backgrounds. The findings reveal a consensus on the importance of human research for generating evidence, yet highlight significant obstacles, including perceived validity issues and resource constraints, particularly among Technical researchers who are less inclined to value or collaborate on human-centered methods. The paper concludes with recommendations aimed at enhancing the epistemic fit of human research within AISE and addressing the limitations faced by researchers in this domain.
Despite a shared belief in the value of human research for AI safety, experts reveal deep-seated barriers that hinder its adoption across the field.
Safety risks of AI are becoming increasingly evident in human interactions with AI technologies. The prominent approaches to evaluating these risks favor technical methods, such as model benchmarks and LLM simulations, often sidelining empirical research with human subjects. To examine this apparent gap in the acceptance of human research, we conduct an expert survey (n=93) and expert interviews (n=17) with AI Safety&Ethics (AISE) researchers from Technical, Sociotechnical, Governance, and Normative backgrounds. Our findings suggest that although there is a consensus that human research is valuable for generating evidence for AISE, its adoption and acceptance are constrained by perceived validity issues, tangible resource barriers, epistemic and personal preferences in methods, and infrastructural constraints from the broader research community. In particular, Technical researchers tend to value human research less and collaborate across disciplines less, suggesting an epistemic tension towards human methods. We propose recommendations for establishing the epistemic fit of human research within AISE and bridging the prohibitive limitations that researchers face, while avoiding performative'human-washing'.
