Large swathes of America remain unconnected to digital services. Lack of digital equity and access persist despite twenty years of policies striving to close the “digital divide.” This divide is more crucial now than ever, as we learn, work, live, and play on digital platforms. The Institute’s work promoting digital equity and access includes participating in and hosting the first convening of the Public Interest Technology University Network, (“PIT-UN”), testifying before Congress and state legislatures on digital equity and Internet adoption rates, and convening symposia highlighting equity issues at the intersections of artificial intelligence R&D, broadband deployment, and Internet use. The Institute further supports educational offerings at Georgetown, including the Technology & Communications Clinic, which represents public-interest clients in telecommunications and digital-equity matters.

July 29, 2020 – Tech Institute Co-hosts Symposium on Bias and Government Artificial Intelligence

Last Wednesday, the Institute for Technology Law & Policy hosted a symposium in collaboration with the Administrative Conference of the United States (ACUS) titled Bias and Government Artificial Intelligence. The symposium, held virtually, was the third of a four-part summer series exploring the use of Artificial Intelligence (AI) and Machine Learning (ML) in federal government agencies.

Chai Feldblum, Partner at Morgan Lewis & Bockius, former Commissioner of the U.S. Equal Employment Opportunity Commission (EEOC), and Public Member of ACUS, moderated the panel. She welcomed panelists:

David Super, Carmack Waterhouse Professor of Law and Economics, Georgetown University Law Center

Kristin Johnson, McGlinchey Stafford Professor of Law, Tulane University Law School

Alex Givens, President, and Chief Executive Officer, Center for Democracy & Technology

The panel discussed how AI systems can encode implicit bias and how federal program managers can protect against bias when they rely on AI systems. Panelists also discussed ways in which government can use AI to detect and counter biased behavior by regulated entities.

Each panelist presented a case study in which bias in government AI systems significantly impacted vulnerable populations within the United States. Professor Johnson began by discussing the ways in which the use of alternative data by AI in consumer credit-scoring platforms may advantage or disadvantage certain groups of people, leading to disparate impacts toward legally protected classes.

Ms. Givens highlighted the ways in which simple design and data entry errors in AI systems can result in devastating human impacts among Medicaid recipients. Similarly, Professor Super discussed how AI has been used to deny Supplemental Nutrition Assistance Program (SNAP) benefits to some of the United States’ most vulnerable populations among us, such as immigrants. Together, these case studies highlight the challenges associated with government’s reliance on AI and the need for appropriate checks and balances.