Real-world applications of machine learning tools in high-stakes domains are often
regulated to be fair, in the sense that the predicted target should satisfy some quantitative …

Algorithms are now routinely used to make consequential decisions that affect human lives.
Examples include college admissions, medical interventions or law enforcement. While …

The potential for learned models to amplify existing societal biases has been broadly
recognized. Fairness-aware classifier constraints, which apply equality metrics of …

Now that machine learning algorithms lie at the center of many important resource allocation
pipelines, computer scientists have been unwittingly cast as partial social planners. Given …

A central goal of algorithmic fairness is to reduce bias in automated decision making. An
unavoidable tension exists between accuracy gains obtained by using sensitive information …

With the growing adoption of machine learning (ML) systems in areas like law enforcement,
criminal justice, finance, hiring, and admissions, it is increasingly critical to guarantee the …

Devising a fair classifier that does not discriminate against different groups is an important
problem in machine learning. Although researchers have proposed various ways of defining …

Kearns, Neel, Roth, and Wu [ICML 2018] recently proposed a notion of rich subgroup
fairness intended to bridge the gap between statistical and individual notions of fairness …

Society increasingly relies on machine learning models for automated decision making. Yet,
efficiency gains from automation have come paired with concern for algorithmic …

As algorithmic decision-making systems are becoming more pervasive, it is crucial to ensure
such systems do not become mechanisms of unfair discrimination on the basis of gender …