The use of algorithmic (learning-based) decision making in scenarios that affect human lives
has motivated a number of recent studies to investigate such decision making systems for …

In past work on fairness in machine learning, the focus has been on forcing the prediction of
classifiers to have similar statistical properties for people of different demographics. To …

We address the problem of algorithmic fairness: ensuring that the outcome of a classifier is
not biased towards certain values of sensitive variables such as age, race or gender. As …

Group fairness, a class of fairness notions that measure how different groups of individuals
are treated differently according to their protected attributes, has been shown to conflict with …

Despite the wide adoption of classification algorithms in many fields, their predictions may
hurt the benefit of some people due to the ubiquitous bias over sensitive features, such as …

Extensive efforts have been made to understand and improve the fairness of machine
learning models based on observational metrics, especially in high-stakes domains such as …

We tackle the problem of group fairness in classification, where the objective is to learn
models that do not unjustly discriminate against subgroups of the population. Most existing …

In classification problems, individual fairness prevents discrimination against individuals
based on protected attributes. Fairness-aware methods usually consist of two stages, first …

As machine learning becomes prevalent in a widening array of sensitive applications such
as job hiring and criminal justice, one critical aspect that machine learning classifiers should …

Machine learning algorithms are extensively used to make increasingly more consequential
decisions about people, so achieving optimal predictive performance can no longer be the …