Administrative Law Commons^™

Getting The Blend Right: Public-Private Partnerships In Risk Management, Cary Coglianese

All Faculty Scholarship

The question of whether there is too much or too little regulation in the United States has driven much political debate for decades. The more important question, though, is not about getting the right amount of regulation but it is about finding the best ways for the public and private sectors to interact. When it comes to managing risk in society, this latter question is necessarily one of choosing between different kinds of structures—or partnerships—between public and private institutions. Sometimes these partnerships are adversarial, as they can be with government regulation. Other times they are seemingly invisible, such as when …

Teaching Voluntary Codes And Standards To Law Students, Cary Coglianese, Caroline Raschbaum

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Voluntary codes and standards issued by nongovernmental institutions affect many aspects of legal work and daily life. Although these codes and standards are voluntary—that is, they are not directly enforceable through civil or criminal penalties—they can and do often shape behavior. Codes and standards inform business practices and product designs. They affect the provisions of contracts and the licensing of patents. And, among still other uses, they affect the handling of evidence in criminal law matters.

More broadly, voluntary codes and standards can play a role similar to, or even take the place of, government regulations. Regulators regularly defer to …

Transparency And Algorithmic Governance, Cary Coglianese, David Lehr

All Faculty Scholarship

Machine-learning algorithms are improving and automating important functions in medicine, transportation, and business. Government officials have also started to take notice of the accuracy and speed that such algorithms provide, increasingly relying on them to aid with consequential public-sector functions, including tax administration, regulatory oversight, and benefits administration. Despite machine-learning algorithms’ superior predictive power over conventional analytic tools, algorithmic forecasts are difficult to understand and explain. Machine learning’s “black-box” nature has thus raised concern: Can algorithmic governance be squared with legal principles of governmental transparency? We analyze this question and conclude that machine-learning algorithms’ relative inscrutability does not pose a …