Regulating Machine Learning

The Biden administration recently announced several artificial intelligence-related actions, including the upcoming release of the Office of Management and Budget’s draft policy guidance on the use of AI systems by the federal government.

As the nation’s foremost expert on governmental use of AI, Cary Coglianese, Edward B. Shils Professor of Law and Professor of Political Science, said:

The Biden Administration’s recent actions to spotlight concerns around artificial intelligence (AI) are coming at an important time. We are seeing rapid advances in and new uses for AI technology, including uses by the federal government. A full and inclusive public dialogue is warranted, as the time has come for clearer guidance for government agencies as well as appropriate regulatory standards for the private sector.

Cary Coglianese, Edward B. Shils Professor of Law and Professor of Political ScienceCoglianese’s recent paper, “Regulating Machine Learning: The Challenge of Heterogeneity,” published by Competition Policy International, argues that because machine-learning algorithms are so dissimilar and diverse—that is, they are heterogeneous—figuring out the best way to regulate their use will be challenging. Regulation must be agile, flexible, and vigilant to address differences in machine-learning algorithms, Coglianese writes, and he urges policymakers to adapt preexisting areas of expertise and authority to address problems created by machine learning.

From the abstract:

New technologies bring with them many promises, but also a series of new problems. Even though these problems are new, they are not unlike the types of problems that regulators have long addressed in other contexts. The lessons from regulation in the past can thus guide regulatory efforts today. Regulators must focus on understanding the problems they seek to address and the causal pathways that lead to these problems. Then they must undertake efforts to shape the behavior of those in industry so that private sector managers focus on their technologies’ problems and take actions to interrupt the causal pathways. This means that regulatory organizations need to strengthen their own technological capacities; however, they need most of all to build their human capital. Successful regulation of technological innovation rests with top quality people who possess the background and skills needed to understand new technologies and their problems.

Coglianese recently presented his paper at a research seminar on contemporary challenges in regulatory governance held by Dublin, Ireland’s UCD College of Social Sciences and Law.

Coglianese, Director of the Penn Program on Regulation (PPR), is a globally renowned expert on regulatory law, analysis, and management who has produced extensive action-oriented research and scholarship. He has consulted with regulatory organizations around the world and is a founding editor of the peer-reviewed journal Regulation & Governance. He also created and continues to serve as the faculty advisor to the PPR’s flagship publication, The Regulatory Review.

Read Coglianese’s full paper on regulating machine learning.

Read more of Coglianese’s scholarship on governmental use of artificial intelligence.