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SLIDE: A surrogate fairness constraint to ensure fairness consistency
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Web of Science
Cited 1 time in Scopus
- Authors
- Issue Date
- 2022-10
- Publisher
- Pergamon Press Ltd.
- Citation
- Neural Networks, Vol.154, pp.441-454
- Abstract
- © 2022 Elsevier LtdAs they take a crucial role in social decision makings, AI algorithms based on ML models should be not only accurate but also fair. Among many algorithms for fair AI, learning a prediction ML model by minimizing the empirical risk (e.g., cross-entropy) subject to a given fairness constraint has received much attention. To avoid computational difficulty, however, a given fairness constraint is replaced by a surrogate fairness constraint as the 0–1 loss is replaced by a convex surrogate loss for classification problems. In this paper, we investigate the validity of existing surrogate fairness constraints and propose a new surrogate fairness constraint called SLIDE, which is computationally feasible and asymptotically valid in the sense that the learned model satisfies the fairness constraint asymptotically and achieves a fast convergence rate. Numerical experiments confirm that the SLIDE works well for various benchmark datasets.
- ISSN
- 0893-6080
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