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At the Duke Law Journal, Prof. Cary Coglianese and Alicia Lai L’21 offer a framework for determining when government should use artificial intelligence

March 10, 2022

Coglianese and Lai caution that existing processes can sometimes be “far more problematic than their digital counterparts.”

In a pathbreaking article recently published in the Duke Law Journal, Cary Coglianese, Edward B. Shils Professor of Law and Professor of Political Science, and Alicia Lai L’21 explore governmental reliance on digital algorithms, concluding that “public officials should proceed with care on a case-by-case basis” when deciding whether to employ digital algorithms, such as machine learning, in place of what they refer to as “human algorithms.”

In their article “Algorithm vs. Algorithm,” Coglianese and Lai write that decision-making about artificial intelligence ought to be predicated on the acknowledgement “that government is already driven by algorithms of arguably greater complexity and potential for abuse: the algorithms implicit in human decision-making.” Humans “operate via algorithms too,” which they explain are reflected in governmental processes — including administrative procedures.

“Yet these human algorithms undeniably fail and are far from transparent,” write Coglianese and Lai. “On an individual level, human decision-making suffers from memory limitations, fatigue, cognitive biases, and racial prejudices, among other problems. On an organizational level, humans succumb to groupthink and free-riding, along with other collective dysfunctionalities. As a result, human decisions will in some cases prove far more problematic than their digital counterparts. Digital algorithms, such as machine learning, can improve governmental performance by facilitating outcomes that are more accurate, timely, and consistent.”

Accordingly, the authors advocate the use of a “case-by-case” analysis of when to deploy digital algorithms to perform tasks currently completed by humans.

Public officials “should consider both whether a particular use would satisfy the basic preconditions for successful machine learning and whether it would in fact lead to demonstrable improvements over the status quo,” write Coglianese and Lai. “The question about the future of public administration is not whether digital algorithms are perfect. Rather, it is a question about what will work better: human algorithms or digital ones.”

An expert in administrative law, Coglianese is the Director of the Penn Program on Regulation and specializes in the study of administrative law and regulatory processes, with an emphasis on the empirical evaluation of alternative processes and strategies and the role of public participation, technology, and business-government relations in policy-making. He is the founding editor of the peer-reviewed journal Regulation & Governance, and he founded and continues to serve as advisor to The Regulatory Review.

Lai is currently a judicial law clerk with the United States Court of Appeals for the Federal Circuit. During her time at the Law School, she served as an Articles Editor for the University of Pennsylvania Law Review, Senior Editor for the Journal of Law & Innovation, co-president of the Penn Intellectual Property Group, and president of the Penn Law Mock Trial Association. Lai was also the recipient of the prestigious Jan Jancin Award, which recognizes students from diverse backgrounds who have made exceptional contributions to the field of intellectual property and intend to continue to pursue a career in the field.

Read Coglianese and Lai’s full article that appears in the Duke Law Journal.

Read more of Coglianese’s scholarship on law and technology, administrative law, regulatory processes, climate change, and more.