Glaze et al. on AI for Adjudication in the Social Security Administration

Kurt Glaze (US Gov – SSA), Daniel E. Ho (Stanford Law School), Gerald K. Ray (SSA), and Christine Tsang (Stanford Law School) have posted “Artificial Intelligence for Adjudication: The Social Security Administration and AI Governance” (Oxford University Press, Handbook on AI Governance (Forthcoming) on SSRN. Here is the abstract:

Despite widespread skepticism of data analytics and artificial intelligence (AI) in adjudication, the Social Security Administration (SSA) pioneered path breaking AI tools that became embedded in multiple levels of its adjudicatory process. How did this happen? What lessons can we draw from the SSA experience for AI in government?

We first discuss how early strategic investments by the SSA in data infrastructure, policy, and personnel laid the groundwork for AI. Second, we document how SSA overcame a wide range of organizational barriers to develop some of the most advanced use cases in adjudication. Third, we spell out important lessons for AI innovation and governance in the public sector. We highlight the importance of leadership to overcome organizational barriers, “blended expertise” spanning technical and domain knowledge, operational data, early piloting, and continuous evaluation. AI should not be conceived of as a one-off IT product, but rather as part of continuous improvement. AI governance is quality assurance.