Stable and Fair Classification

By Lingxiao Huang, Nisheeth K. Vishnoi. 2019

74 citations recorded by the source corpus.

Fair classification has been a topic of intense study in machine learning, and several algorithms have been proposed towards this important task. However, in a recent study, Friedler et al. observed that fair classification algorithms may not be stable with respect to variations in the training dataset -- a crucial consideration in several real-world applications. Motivated by their work, we study the problem of designing classification algorithms that are both fair and stable. We propose an extended framework based on fair classification algorithms that are formulated as optimization problems, by introducing a stability-focused regularization term. Theoretically, we prove a stability guarantee, that was lacking in fair classification algorithms, and also provide an accuracy guarantee for our extended framework. Our accuracy guarantee can be used to inform the selection of the regularization parameter in our framework. To the best of our knowledge, this is the first work that combines stability and fairness in automated decision-making tasks. We assess the benefits of our approach empirically by extending several fair classification algorithms that are shown to achieve the best balance between fairness and accuracy over the Adult dataset. Our empirical results show that our framework indeed improves the stability at only a slight sacrifice in accuracy.

Subfields: Artificial Intelligence, Pediatrics, Perinatology and Child Health, Statistics and Probability, AI Ethics, Fairness & Society

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Citations in the corpus

A Survey on Bias and Fairness in Machine Learning (2019, methodology): “Other methods try to not only satisfy some fairness constraints but to also be stable toward change in the test set [59].”
Trustworthy Artificial Intelligence: A Review (2022, background): “Decrease F [10, 27, 33, 38, 97, 128, 172] F, P [63, 91, 169, 189] F, A [156]”
Fairness Violations and Mitigation under Covariate Shift (2019, background): “To mitigate this, Huang and Vishnoi [27] propose adding a regularization term to the constrained ERM problem that guarantees stability.”
Fairness in Machine Learning: A Survey (2020, background): “Commentary: References [109, 145] note that often approaches for fair ML are not stable, i.e., subtle changes in the training data significantly affect performance (comparatively high standard deviation).”