Anti-discrimination is an increasingly important task in data science. In this paper, we investigate
the problem of discovering both direct and indirect discrimination from the historical data…

A recent trend of fair machine learning is to define fairness as causality-based notions which
concern the causal connection between protected attributes and decisions. However, one …

Predictive models learned from historical data are widely used to help companies and
organizations make decisions. However, they may digitally unfairly treat unwanted groups, raising …

Fairness-aware learning studies the problem of building machine learning models that are
subject to fairness requirements. Counterfactual fairness is a notion of fairness derived from …

Non-small cell lung cancer (NSCLC) is highly prevalent and needs novel therapies.
Melanoma-associated antigen 3 (MAGE-A3) is a lung cancer antigen and calreticulin (CALR) can …

Achieving fairness in learning models is currently an imperative task in machine learning.
Meanwhile, recent research showed that fairness should be studied from the causal …

Anti-discrimination is an increasingly important task in data science. In this paper, we investigate
the problem of discovering both direct and indirect discrimination from the historical data…

Discrimination discovery and prevention/removal are increasingly important tasks in data
mining. Discrimination discovery aims to unveil discriminatory practices on the protected …

In this paper, we study the fairness-aware classification problem by formulating it as a
constrained optimization problem. Several limitations exist in previous works due to the lack of a …

Discrimination discovery is to unveil discrimination against a specific individual by
analyzing the historical dataset. In this paper, we develop a general technique to capture …