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 …

Objective: One concern about the use of risk assessment instruments in legal decisions is
the potential for disparate impact by race or ethnicity. This means that one racial or ethnic …

Machine learning algorithms are becoming ubiquitous in modern life. When used to help
inform human decision making, they have been criticized by some for insufficient accuracy …

ABSTRACT While Artificial Intelligence (AI) algorithms have achieved performance levels
comparable to human experts on various predictive tasks, human experts can still access …

Recent work highlights the role of causality in designing equitable decision-making
algorithms. It is not immediately clear, however, how existing causal conceptions of fairness …

We evaluate the impacts of adopting algorithmic risk assessments in sentencing. We find
that judges changed sentencing practices in response to the risk assessment, but that …

In consequential domains, it is often impossible to compel individuals to take treatment, so
that optimal policy rules are merely suggestions in the presence of human non-adherence to …

Predictive algorithms are now commonly used to distribute society's resources and
sanctions. But these algorithms can entrench and exacerbate inequities. To guard against …

In designing risk assessment algorithms, many scholars promote a “kitchen sink” approach,
reasoning that more information yields more accurate predictions. We show, however, that …

Algorithmic and data-driven decisions and recommendations are commonly used in high-
stakes decision-making settings such as criminal justice, medicine, and public policy. We …