Fair machine learning has become a significant research topic with broad societal impact.
However, most fair learning methods require direct access to personal demographic data …

Fairness and privacy are both significant social norms in machine learning. In (Hu et al
2019), we propose a distributed framework to learn fair prediction models while protecting …

Machine learning models have demonstrated promising performance in many areas.
However, the concerns that they can be biased against specific demographic groups hinder …

Fairness-aware learning aims at constructing classifiers that not only make accurate
predictions, but also do not discriminate against specific groups. It is a fast-growing area of …

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 …

Fair machine learning methods seek to train models that balance model performance across
demographic subgroups defined over sensitive attributes like race and gender. Although …

Recently, lots of algorithms have been proposed for learning a fair classifier from
decentralized data. However, many theoretical and algorithmic questions remain open. First …

We propose differential fairness, a multi-attribute definition of fairness in machine learning
which is informed by intersectionality, a critical lens arising from the humanities literature …

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 …

Machine learning algorithms are increasingly used to make or support important decisions
about people's lives. This has led to interest in the problem of fair classification, which …