Law Commons^™

Title 2.0: Discrimination Law In A Data-Driven Society, Bryan Casey

Journal of Law and Mobility

More than a quarter century after civil rights activists pioneered America’s first ridesharing network, the connections between transportation, innovation, and discrimination are again on full display. Industry leaders such as Uber, Amazon, and Waze have garnered widespread acclaim for successfully combatting stubbornly persistent barriers to transportation. But alongside this well-deserved praise has come a new set of concerns. Indeed, a growing number of studies have uncovered troubling racial disparities in wait times, ride cancellation rates, and service availability in companies including Uber, Lyft, Task Rabbit, Grubhub, and Amazon Delivery.

Surveying the methodologies employed by these studies reveals a subtle, but …

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. …

Robot Criminals, Ying Hu

University of Michigan Journal of Law Reform

When a robot harms humans, are there any grounds for holding it criminally liable for its misconduct? Yes, provided that the robot is capable of making, acting on, and communicating the reasons behind its moral decisions. If such a robot fails to observe the minimum moral standards that society requires of it, labeling it as a criminal can effectively fulfill criminal law’s function of censuring wrongful conduct and alleviating the emotional harm that may be inflicted on human victims.

Imposing criminal liability on robots does not absolve robot manufacturers, trainers, or owners of their individual criminal liability. The former is …

Power, Process, And Automated Decision-Making, Ari Ezra Waldman

Articles & Chapters

Many decisions that used to be made by humans are now made by machines. And yet, automated decision-making systems based on “big data” – powered algorithms and machine learning are just as prone to mistakes, biases, and arbitrariness as their human counterparts. The result is a technologically driven decision-making process that seems to defy interrogation, analysis, and accountability and, therefore, undermines due process. This should make algorithmic decision-making an illegitimate source of authority in a liberal democracy. This Essay argues that algorithmic decision-making is a product of the neoliberal project to undermine social values like equality, nondiscrimination, and human flourishing …

Inside The Black Box Of Search Algorithms, Susan Nevelow Mart, Joe Breda, Ed Walters, Tito Sierra, Khalid Al-Kofahi

Publications

A behind-the-scenes look at the algorithms that rank results in Bloomberg Law, Fastcase, Lexis Advance, and Westlaw.

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 …