Achieving Fairness at No Utility Cost via Data Reweighing with Influence

• URL: http://arxiv.org/abs/2202.00787v2
• Date: Fri, 17 Jun 2022 03:47:02 GMT
• Title: Achieving Fairness at No Utility Cost via Data Reweighing with Influence
• Authors: Peizhao Li and Hongfu Liu
• Abstract summary: We propose a data reweighing approach that only adjusts the weight for samples in the training phase. We granularly model the influence of each training sample with regard to fairness-related quantity and predictive utility. Our approach can empirically release the tradeoff and obtain cost-free fairness for equal opportunity.
• Score: 27.31236521189165
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: With the fast development of algorithmic governance, fairness has become a compulsory property for machine learning models to suppress unintentional discrimination. In this paper, we focus on the pre-processing aspect for achieving fairness, and propose a data reweighing approach that only adjusts the weight for samples in the training phase. Different from most previous reweighing methods which usually assign a uniform weight for each (sub)group, we granularly model the influence of each training sample with regard to fairness-related quantity and predictive utility, and compute individual weights based on influence under the constraints from both fairness and utility. Experimental results reveal that previous methods achieve fairness at a non-negligible cost of utility, while as a significant advantage, our approach can empirically release the tradeoff and obtain cost-free fairness for equal opportunity. We demonstrate the cost-free fairness through vanilla classifiers and standard training processes, compared to baseline methods on multiple real-world tabular datasets. Code available at https://github.com/brandeis-machine-learning/influence-fairness.

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