SURE: Robust, Explainable, and Fair Classification without Sensitive Attributes

A classifier that is accurate on average may still underperform for "sensitive" subsets of people. Such subsets could be based on race, gender, age, etc. The goal of a fair classifier is to perform well for all sensitive subsets. But often, the sensitive subsets are not known a priori. So we may want the classifier to perform well on all subsets that are likely to be sensitive. We propose an iterative algorithm called SURE for this problem. In each iteration, SURE identifies high-risk zones in feature space where the risk of unfair classification is statistically significant. By changing the loss function's weights for points from these zones, SURE builds a fair classifier. The emphasis on statistical significance makes SURE robust to noise. The high-risk zones are intuitive and interpretable. Every step of our method is explainable in terms of significance tests. Finally, SURE is fast and parameter-free. Experiments on both simulated and real-world datasets show that SURE is competitive with the state-of-the-art.