Although some have heralded recent political and cultural developments as signaling the
arrival of a postracial era in America, several legal and social controversies regarding …

In a time when racial prejudice is generally taboo and decision makers, including law
enforcement officials, strenuously disavow the use of group‐based stereotypes to make …

The field of fair machine learning aims to ensure that decisions guided by algorithms are
equitable. Over the last decade, several formal, mathematical definitions of fairness have …

Data from 25 large US cities is assembled to estimate the impact of the onset of the COVID-
19 pandemic on crime. There is a widespread immediate drop in both criminal incidents and …

Disparities by race, gender, and other protected characteristics have been widely
documented in many important settings—such as employment, housing, criminal justice …

Fairness in machine learning has predominantly been studied in static classification settings
without concern for how decisions change the underlying population over time …

Proactive policing, as a strategic approach used by police agencies to prevent crime, is a
relatively new phenomenon in the United States. It developed from a crisis in confidence in …

We assessed racial disparities in policing in the United States by compiling and analysing a
dataset detailing nearly 100 million traffic stops conducted across the country. We found that …

We review the empirical literature in economics on discrimination in the labor market and
criminal justice system, focusing primarily on discrimination by race. We then discuss …

Much of the debate on the impact of algorithms is concerned with fairness, defined as the
absence of discrimination for individuals with the same" merit." Drawing on the theory of …