PENN LAW — 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.”