In recent years, a growing body of work has emerged on how to learn machine learning
models under fairness constraints, often expressed with respect to some sensitive attributes …

Machine Learning (ML) models trained on biased data can reproduce and even amplify
these biases. Since such models are deployed to make decisions that can affect people's …

Fairness is a growing concern in machine learning as state-of-the-art models may amplify
social prejudice by making biased predictions against specific demographics such as race …

Most existing fair classifiers rely on sensitive attributes to achieve fairness. However, for
many scenarios, we cannot obtain sensitive attributes due to privacy and legal issues. The …

Despite the rapid development and great success of machine learning models, extensive
studies have exposed their disadvantage of inheriting latent discrimination and societal bias …

The vast majority of techniques to train fair models require access to the protected attribute
(eg, race, gender), either at train time or in production. However, in many practically …

We introduce a method, MMD-B-Fair, to learn fair representations of data via kernel two-
sample testing. We find neural features of our data where a maximum mean discrepancy …

Modern machine learning (ML) models are becoming increasingly popular and are widely
used in decision-making systems. However, studies have shown critical issues of ML …

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

In this paper, we propose FairNN a neural network that performs joint feature representation
and classification for fairness-aware learning. Our approach optimizes a multi-objective loss …