As AI systems develop in complexity it is becoming increasingly hard to ensure non-
discrimination on the basis of protected attributes such as gender, age, and race. Many …

The adoption of machine learning in applications where it is crucial to ensure fairness and
accountability has led to a large number of model proposals in the literature, largely …

Making predictions that are fair with regard to protected attributes (race, gender, age, etc.)
has become an important requirement for classification algorithms. Existing techniques …

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

Understanding and removing bias from the decisions made by machine learning models is
essential to avoid discrimination against unprivileged groups. Despite recent progress in …

Machine learning (ML) may be oblivious to human bias but it is not immune to its
perpetuation. Marginalisation and iniquitous group representation are often traceable in the …

In this paper, we study the prediction of a real-valued target, such as a risk score or
recidivism rate, while guaranteeing a quantitative notion of fairness with respect to a …

We provide an overview of the state-of-the-art in fairnessaware machine learning and
examine a wide variety of research articles in the area. We survey different fairness notions …

Computers are increasingly used to make decisions that have significant impact on people's
lives. Often, these predictions can affect different population subgroups disproportionately …

The unfairness of a regressor is evaluated by measuring the correlation between the
estimator and the sensitive attribute (eg, race, gender, age), and the coefficient of …