Robustness and fairness are two equally important issues for machine learning systems.
Despite the active research on robustness and fairness of ML recently, these efforts focus on …

The fairness in machine learning is getting increasing attention, as its applications in
different fields continue to expand and diversify. To mitigate the discriminated model …

We present GetFair, a novel framework for tuning fairness of classification models. The fair
classification problem deals with training models for a given classification task where data …

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 …

A central goal of algorithmic fairness is to reduce bias in automated decision making. An
unavoidable tension exists between accuracy gains obtained by using sensitive information …

In recent years, machine learning has become more common in everyday applications.
Consequently, numerous studies have explored issues of unfairness against specific groups …

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

Unintended biases in machine learning (ML) models are among the major concerns that
must be addressed to maintain public trust in ML. In this paper, we address process fairness …

Trustworthy AI is a critical issue in machine learning where, in addition to training a model
that is accurate, one must consider both fair and robust training in the presence of data bias …

Fair representation learning (FRL) is a popular class of methods aiming to produce fair
classifiers via data preprocessing. Recent regulatory directives stress the need for FRL …