Racial disparities in the criminal justice system are well documented and widespread. The
present review examines racial disparities in three areas of the system: policing, prison …

Big data and data science are transforming the world in ways that spawn new concerns for
social scientists, such as the impacts of the internet on citizens and the media, the …

Fairness is an increasingly important concern as machine learning models are used to
support decision making in high-stakes applications such as mortgage lending, hiring, and …

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 …

Peer review is a well-established cornerstone of the scientific process, yet it is not immune to
biases like status bias, which we explore in this paper. Merton described this bias as …

'QuantCrit'(Quantitative Critical Race Theory) is a rapidly developing approach that seeks to
challenge and improve the use of statistical data in social research by applying the insights …

A software developer's misadventures in computer programming, machine learning, and
artificial intelligence reveal why we should never assume technology always get it right. In …

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

Data‐driven algorithms are widely used to make or assist decisions in sensitive domains,
including healthcare, social services, education, hiring, and criminal justice. In various …

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 …