Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness

• URL: http://arxiv.org/abs/2011.08398v1
• Date: Tue, 17 Nov 2020 03:25:44 GMT
• Title: Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness
• Authors: Tong Wang and Maytal Saar-Tsechansky
• Abstract summary: We propose a model-agnostic approach for mitigating the prediction bias of a black-box decision-maker. Our method detects in the feature space where the black-box decision-maker is biased and replaces it with a few short decision rules, acting as a "fair surrogate"
• Score: 10.53972370889201
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a model-agnostic approach for mitigating the prediction bias of a black-box decision-maker, and in particular, a human decision-maker. Our method detects in the feature space where the black-box decision-maker is biased and replaces it with a few short decision rules, acting as a "fair surrogate". The rule-based surrogate model is trained under two objectives, predictive performance and fairness. Our model focuses on a setting that is common in practice but distinct from other literature on fairness. We only have black-box access to the model, and only a limited set of true labels can be queried under a budget constraint. We formulate a multi-objective optimization for building a surrogate model, where we simultaneously optimize for both predictive performance and bias. To train the model, we propose a novel training algorithm that combines a nondominated sorting genetic algorithm with active learning. We test our model on public datasets where we simulate various biased "black-box" classifiers (decision-makers) and apply our approach for interpretable augmented fairness.

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