Fairness for Machine Learning has received considerable attention, recently. Various
mathematical formulations of fairness have been proposed, and it has been shown that it is …

We review and reflect on fairness notions proposed in machine learning literature and make
an attempt to draw connections to arguments in moral and political philosophy, especially …

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 …

Fairness emerged as an important requirement to guarantee that Machine Learning (ML)
predictive systems do not discriminate against specific individuals or entire sub-populations …

Individual fairness is an intuitive definition of algorithmic fairness that addresses some of the
drawbacks of group fairness. Despite its benefits, it depends on a task specific fair metric that …

An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging …

In recent years fairness in machine learning (ML) has emerged as a highly active area of
research and development. Most define fairness in simple terms, where fairness means …

Abstract Machine learning algorithms can now identify patterns and correlations in (big)
datasets and predict outcomes based on the identified patterns and correlations. They can …

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

Machine Learning (ML) based predictive systems are increasingly used to support decisions
with a critical impact on individuals' lives such as college admission, job hiring, child …