A Fair Empirical Risk Minimization with Generalized Entropy
| Autor: | Jin, Youngmi, Gim, Jio, Lee, Tae-Jin, Suh, Young-Joo |
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| Rok vydání: | 2022 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | This paper studies a parametric family of algorithmic fairness metrics, called generalized entropy, which originally has been used in public welfare and recently introduced to machine learning community. As a meaningful metric to evaluate algorithmic fairness, it requires that generalized entropy specify fairness requirements of a classification problem and the fairness requirements should be realized with small deviation by an algorithm. We investigate the role of generalized entropy as a design parameter for fair classification algorithm through a fair empirical risk minimization with a constraint specified in terms of generalized entropy. We theoretically and experimentally study learnability of the problem. Comment: 56pages and 92 figures Revised for adding experimental results |
| Databáze: | arXiv |
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