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Full-Text Articles in Law
Regulating Machine Learning: The Challenge Of Heterogeneity, Cary Coglianese
Regulating Machine Learning: The Challenge Of Heterogeneity, Cary Coglianese
All Faculty Scholarship
Machine learning, or artificial intelligence, refers to a vast array of different algorithms that are being put to highly varied uses, including in transportation, medicine, social media, marketing, and many other settings. Not only do machine-learning algorithms vary widely across their types and uses, but they are evolving constantly. Even the same algorithm can perform quite differently over time as it is fed new data. Due to the staggering heterogeneity of these algorithms, multiple regulatory agencies will be needed to regulate the use of machine learning, each within their own discrete area of specialization. Even these specialized expert agencies, though, …
Administrative Law In The Automated State, Cary Coglianese
Administrative Law In The Automated State, Cary Coglianese
All Faculty Scholarship
In the future, administrative agencies will rely increasingly on digital automation powered by machine learning algorithms. Can U.S. administrative law accommodate such a future? Not only might a highly automated state readily meet longstanding administrative law principles, but the responsible use of machine learning algorithms might perform even better than the status quo in terms of fulfilling administrative law’s core values of expert decision-making and democratic accountability. Algorithmic governance clearly promises more accurate, data-driven decisions. Moreover, due to their mathematical properties, algorithms might well prove to be more faithful agents of democratic institutions. Yet even if an automated state were …
Transparency And Algorithmic Governance, Cary Coglianese, David Lehr
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 …
Deliberative Public Engagement With Science: An Empirical Investigation, Lisa M. Pytlikzillig, Myiah J. Hutchens, Peter Muhlberger, Frank J. Gonzalez, Alan Tomkins
Deliberative Public Engagement With Science: An Empirical Investigation, Lisa M. Pytlikzillig, Myiah J. Hutchens, Peter Muhlberger, Frank J. Gonzalez, Alan Tomkins
Lisa PytlikZillig Publications
The purpose of this book is to share some results and the data from four studies in which we used experimental procedures to manipulate key features of deliberative public engagement to study the impacts in the context of deliberations about nanotechnology. In this chapter, we discuss the purpose of this book, which is to advance science of public engagement, and the overarching question motivating our research: What public engagement methods work for what purposes and why? We also briefly review existing prior work related to our overarching goal and question and introduce the contents of the rest of the book. …
When The Default Is No Penalty: Negotiating Privacy At The Ntia, Margot E. Kaminski
When The Default Is No Penalty: Negotiating Privacy At The Ntia, Margot E. Kaminski
Publications
Consumer privacy protection is largely within the purview of the Federal Trade Commission. In recent years, however, the National Telecommunications and Information Administration (NTIA) at the Department of Commerce has hosted multistakeholder negotiations on consumer privacy issues. The NTIA process has addressed mobile apps, facial recognition, and most recently, drones. It is meant to serve as a venue for industry self-regulation. Drawing on the literature on co-regulation and on penalty defaults, I suggest that the NTIA process struggles to successfully extract industry expertise and participation against a dearth of federal data privacy law and enforcement. This problem is most exacerbated …