Artificial Intelligence for Adjudication: The Social Security Administration and AI Governance

Oxford University Press, Handbook on AI Governance (Forthcoming)

24 Pages Posted: 28 Nov 2021

See all articles by Kurt Glaze

Kurt Glaze

Government of the United States of America - Social Security Administration

Daniel E. Ho

Stanford Law School

Gerald K. Ray

Social Security Administration (Retired)

Christine Tsang

Stanford Law School; Stanford University

Date Written: August 18, 2021

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.

Keywords: artificial intelligence, social security, adjudication, innovation, administrative law

Suggested Citation

Glaze, Kurt and Ho, Daniel E. and Ray, Gerald and Tsang, Christine, Artificial Intelligence for Adjudication: The Social Security Administration and AI Governance (August 18, 2021). Oxford University Press, Handbook on AI Governance (Forthcoming), Available at SSRN: https://ssrn.com/abstract=3935950 or http://dx.doi.org/10.2139/ssrn.3935950

Kurt Glaze

Government of the United States of America - Social Security Administration ( email )

Washington, DC 20254
United States

Daniel E. Ho (Contact Author)

Stanford Law School ( email )

559 Nathan Abbott Way
Stanford, CA 94305-8610
United States
650-723-9560 (Phone)

HOME PAGE: http://dho.stanford.edu

Gerald Ray

Social Security Administration (Retired) ( email )

Christine Tsang

Stanford Law School ( email )

559 Nathan Abbott Way
Stanford, CA 94305
United States

Stanford University ( email )

Stanford, CA 94305
United States

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