Over the past several years, multiple different methods to measure the causal fairness of
machine learning models have been proposed. However, despite the growing number of …

The law forbids discrimination. But the ambiguity of human decision-making often makes it
hard for the legal system to know whether anyone has discriminated. To understand how …

A Turing Award-winning computer scientist and statistician shows how understanding
causality has revolutionized science and will revolutionize artificial intelligence" Correlation …

The automation of decision-making processes via machine learning algorithmic systems
presents itself as a legal paradox. On one hand, such automation is often an attempt to …

Welcome to law's information revolution'-revolution already in 2 progress. While the 2008
financial crisis can be seen as the precipitating event, developments in legal information …

We explore the role of sex in judging by addressing two questions of long‐standing interest
to political scientists: whether and in what ways male and female judges decide cases …

Actuarial risk assessments are frequently touted as a neutral way to counteract implicit bias
and increase the fairness of decisions made at almost every juncture of the criminal justice …

This review examines the role of race in the decennial process of redistricting. I review the
scholarly literature on three related questions: What role should racial redistricting play in …

Since the emergence of the modern class action in the 1966 amendments to the Federal
Rules of Civil Procedure, controversy has attended the certification of litigation to proceed on …

The model of discrimination animating some of the most common approaches to detecting
discrimination in both law and social science-the counterfactual causal model-is wrong. In …