Rawlsian Fair Adaptation of Deep Learning Classifiers

• URL: http://arxiv.org/abs/2105.14890v1
• Date: Mon, 31 May 2021 11:31:30 GMT
• Title: Rawlsian Fair Adaptation of Deep Learning Classifiers
• Authors: Kulin Shah, Pooja Gupta, Amit Deshpande, Chiranjib Bhattacharyya
• Abstract summary: Group-fairness in classification aims for equality of a predictive utility across different sensitive sub-populations, e.g., race or gender. This paper shows that a Rawls classifier minimizes the error rate on the worst-off sensitive sub-population. Our empirical results show significant improvement over state-of-the-art group-fair algorithms, even without retraining for fairness.
• Score: 18.150327860396786
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Group-fairness in classification aims for equality of a predictive utility across different sensitive sub-populations, e.g., race or gender. Equality or near-equality constraints in group-fairness often worsen not only the aggregate utility but also the utility for the least advantaged sub-population. In this paper, we apply the principles of Pareto-efficiency and least-difference to the utility being accuracy, as an illustrative example, and arrive at the Rawls classifier that minimizes the error rate on the worst-off sensitive sub-population. Our mathematical characterization shows that the Rawls classifier uniformly applies a threshold to an ideal score of features, in the spirit of fair equality of opportunity. In practice, such a score or a feature representation is often computed by a black-box model that has been useful but unfair. Our second contribution is practical Rawlsian fair adaptation of any given black-box deep learning model, without changing the score or feature representation it computes. Given any score function or feature representation and only its second-order statistics on the sensitive sub-populations, we seek a threshold classifier on the given score or a linear threshold classifier on the given feature representation that achieves the Rawls error rate restricted to this hypothesis class. Our technical contribution is to formulate the above problems using ambiguous chance constraints, and to provide efficient algorithms for Rawlsian fair adaptation, along with provable upper bounds on the Rawls error rate. Our empirical results show significant improvement over state-of-the-art group-fair algorithms, even without retraining for fairness.

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