Fair Wrapping for Black-box Predictions

• URL: http://arxiv.org/abs/2201.12947v1
• Date: Mon, 31 Jan 2022 01:02:39 GMT
• Title: Fair Wrapping for Black-box Predictions
• Authors: Alexander Soen, Ibrahim Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie
• Abstract summary: We learn a wrapper function which we define as an alpha-tree, which modifies the prediction. We show that our modification has appealing properties in terms of composition ofalpha-trees, generalization, interpretability, and KL divergence between modified and original predictions.
• Score: 105.10203274098862
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a new family of techniques to post-process ("wrap") a black-box classifier in order to reduce its bias. Our technique builds on the recent analysis of improper loss functions whose optimisation can correct any twist in prediction, unfairness being treated as a twist. In the post-processing, we learn a wrapper function which we define as an {\alpha}-tree, which modifies the prediction. We provide two generic boosting algorithms to learn {\alpha}-trees. We show that our modification has appealing properties in terms of composition of{\alpha}-trees, generalization, interpretability, and KL divergence between modified and original predictions. We exemplify the use of our technique in three fairness notions: conditional value at risk, equality of opportunity, and statistical parity; and provide experiments on several readily available datasets.

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