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 …

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 …

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

Algorithmic risk assessments are increasingly used to help humans make decisions in high-
stakes settings, such as medicine, criminal justice and education. In each of these cases, the …

We propose a novel algorithm for learning fair representations that can simultaneously
mitigate two notions of disparity among different demographic subgroups in the classification …

In domains such as criminal justice, medicine, and social welfare, decision makers
increasingly have access to algorithmic Risk Assessment Instruments (RAIs). RAIs estimate …

To boost the application of machine learning (ML) techniques for credit scoring models, the
blackbox problem should be addressed. The primary aim of this paper is to propose a …

Recommender systems play a key role in shaping modern web ecosystems. These systems
alternate between (1) making recommendations (2) collecting user responses to these …

In the dominant paradigm for designing equitable machine learning systems, one works to
ensure that model predictions satisfy various fairness criteria, such as parity in error rates …

Methods for building fair predictors often involve tradeoffs between fairness and accuracy
and between different fairness criteria. Recent work seeks to characterize these tradeoffs in …