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

Developing learning methods which do not discriminate subgroups in the population is a
central goal of algorithmic fairness. One way to reach this goal is by modifying the data …

To mitigate the bias exhibited by machine learning models, fairness criteria can be
integrated into the training process to ensure fair treatment across all demographics, but it …

Fair representation learning provides an effective way of enforcing fairness constraints
without compromising utility for downstream users. A desirable family of such fairness …

Algorithmic decision making is employed in an increasing number of real-world applications
to aid human decision making. While it has shown considerable promise in terms of …

As machine learning becomes prevalent in a widening array of sensitive applications such
as job hiring and criminal justice, one critical aspect that machine learning classifiers should …

Fairness has become an important topic in machine learning. Generally, most literature on
fairness assumes that the sensitive information, such as gender or race, is present in the …

Fairness in machine learning is getting rising attention as it is directly related to real-world
applications and social problems. Recent methods have been explored to alleviate the …

Real-world applications of machine learning tools in high-stakes domains are often
regulated to be fair, in the sense that the predicted target should satisfy some quantitative …

In algorithmically fair prediction problems, a standard goal is to ensure the equality of
fairness metrics across multiple overlapping groups simultaneously. We reconsider this …