Noise-tolerant fair classification
This addresses the issue of fairness in machine learning for scenarios where sensitive data is unreliable, such as in surveys with concealed identities, though it is incremental as it builds on existing noise models.
The paper tackles the problem of learning fair classifiers when sensitive features are noisy, showing that by adjusting the fairness tolerance using noise-rate estimators, fair classification is still possible, and demonstrates empirical effectiveness on two case-studies.
Fairness-aware learning involves designing algorithms that do not discriminate with respect to some sensitive feature (e.g., race or gender). Existing work on the problem operates under the assumption that the sensitive feature available in one's training sample is perfectly reliable. This assumption may be violated in many real-world cases: for example, respondents to a survey may choose to conceal or obfuscate their group identity out of fear of potential discrimination. This poses the question of whether one can still learn fair classifiers given noisy sensitive features. In this paper, we answer the question in the affirmative: we show that if one measures fairness using the mean-difference score, and sensitive features are subject to noise from the mutually contaminated learning model, then owing to a simple identity we only need to change the desired fairness-tolerance. The requisite tolerance can be estimated by leveraging existing noise-rate estimators from the label noise literature. We finally show that our procedure is empirically effective on two case-studies involving sensitive feature censoring.