Read our related explainer, A Sociotechnical Approach to AI Policy, which explains what a sociotechnical perspective is and why it matters in policy.

Because real-world uses of AI are always embedded within larger social institutions and power dynamics, technical assessments alone are insufficient to govern AI. Technical design, social practices and cultural norms, the context a system is integrated in, and who designed and operates it all impact the performance, failure, benefits, and harms of an AI system. This means that successful AI governance requires expertise in the sociotechnical nature of AI systems. 

Sociotechnical research and approaches have proven crucial to AI development and accountability — the key will be implementing AI governance practices that employ the expertise required to reap these benefits. This policy brief explores the importance of integrating humanities and social science expertise into AI governance, and outlines some of the ways that doing so can help us to assess the performance and mitigate the harms of AI systems. It concludes with a set of recommendations for incorporating humanities and social science methods and expertise into government efforts, including in hiring and procurement processes.