Law Commons^™

Digital Market Perfection, Rory Van Loo

Faculty Scholarship

Google’s, Apple’s, and other companies’ automated assistants are increasingly serving as personal shoppers. These digital intermediaries will save us time by purchasing grocery items, transferring bank accounts, and subscribing to cable. The literature has only begun to hint at the paradigm shift needed to navigate the legal risks and rewards of this coming era of automated commerce. This Article begins to fill that gap first by surveying legal battles related to contract exit, data access, and deception that will determine the extent to which automated assistants are able to help consumers to search and switch, potentially bringing tremendous societal benefits. …

Pharmaceutical Drugs Of Uncertain Value, Lifecycle Regulation At The Us Food And Drug Administration, And Institutional Incumbency, Matthew Herder

Articles, Book Chapters, & Popular Press

Policy Points

• The US Food and Drug Administration (FDA) has in recent years allowed onto the market several drugs with limited evidence of safety and effectiveness, provided that manufacturers agree to carry out additional studies while the drugs are in clinical use.
• Studies suggest that these postmarketing requirements (PMRs) frequently lack transparency, are subject to delays, and fail to answer the questions of greatest clinical importance. Yet, none of the literature speaks directly to the challenges that the FDA—as a regulatory institution—encounters in enforcing PMRs.
• Through a series of interviews with FDA leadership, this article analyzes and situates those challenges …

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 …