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

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

An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence (AI) algorithms. Since they now touch on many aspects of …

Machine learning (ML) is increasingly being used to make decisions in our society. ML
models, however, can be unfair to certain demographic groups (eg, African Americans or …

This work aims to generalize demographic parity to continuous sensitive attributes while
preserving tractable computation. Current fairness metrics for continuous sensitive attributes …

Statistical measures for group fairness in machine learning reflect the gap in performance of
algorithms across different groups. These measures, however, exhibit a high variance …

Algorithmic fairness and privacy are essential pillars of trustworthy machine learning. Fair
machine learning aims at minimizing discrimination against protected groups by, for …

When it is ethical and legal to use a sensitive attribute (such as gender or race) in machine
learning systems, the question remains how to do so. We show that the naive application of …

Clustering is a common technique for data analysis, which aims to partition data into similar
groups. When the data comes from different sources, it is highly desirable to maintain the …

In classification models, fairness can be ensured by solving a constrained optimization
problem. We focus on fairness constraints like Disparate Impact, Demographic Parity, and …