Provably Training Neural Network Classifiers under Fairness Constraints

• URL: http://arxiv.org/abs/2012.15274v1
• Date: Wed, 30 Dec 2020 18:46:50 GMT
• Title: Provably Training Neural Network Classifiers under Fairness Constraints
• Authors: You-Lin Chen, Zhaoran Wang, Mladen Kolar
• Abstract summary: We show that overparametrized neural networks could meet the constraints. Key ingredient of building a fair neural network classifier is establishing no-regret analysis for neural networks.
• Score: 70.64045590577318
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training a classifier under fairness constraints has gotten increasing attention in the machine learning community thanks to moral, legal, and business reasons. However, several recent works addressing algorithmic fairness have only focused on simple models such as logistic regression or support vector machines due to non-convex and non-differentiable fairness criteria across protected groups, such as race or gender. Neural networks, the most widely used models for classification nowadays, are precluded and lack theoretical guarantees. This paper aims to fill this missing but crucial part of the literature of algorithmic fairness for neural networks. In particular, we show that overparametrized neural networks could meet the fairness constraints. The key ingredient of building a fair neural network classifier is establishing no-regret analysis for neural networks in the overparameterization regime, which may be of independent interest in the online learning of neural networks and related applications.

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