Troubling translation: Sociotechnical research in AI policy and governance

As technology companies develop and incorporate artificial intelligence (AI) systems across society, calls for a sociotechnical approach to AI policy and governance have intensified. Sociotechnical research emphasises that understanding the efficacy, harms, and risks of AI requires attention to the cultural, social, and economic conditions that shape these systems. Yet development and regulation often remain split between technical research and sociotechnical work rooted in the humanities and social sciences. Bridging not only disciplinary divides but the gap between research and AI regulation, policy, and governance is crucial to building an AI ecosystem that centres human risk. Researchers are frequently urged to “translate” findings for policymakers, with translation framed as a pathway to interdisciplinary collaboration and evidence-based governance. This paper troubles this notion of translation by examining two case studies: the National Institute of Standards and Technology’s US AI Safety Institute Consortium and the Public Technology Leadership Collaborative. We show that spanning research and policy requires more than simplified communication, but depends on building relationships and navigating the uneven terrain where academics, policymakers, and practitioners meet. We conclude with recommendations for existing governmental mechanisms for incorporating researchers into policymaking, which may not be widely known to academics.