Fair-Net: A Network Architecture For Reducing Performance Disparity Between Identifiable Sub-Populations

• URL: http://arxiv.org/abs/2106.00720v1
• Date: Tue, 1 Jun 2021 18:26:08 GMT
• Title: Fair-Net: A Network Architecture For Reducing Performance Disparity Between Identifiable Sub-Populations
• Authors: Arghya Datta, S. Joshua Swamidass
• Abstract summary: We introduce Fair-Net, a multitask neural network architecture that improves both classification accuracy and probability calibration across identifiable sub-populations. Empirical studies with three real world benchmark datasets demonstrate that Fair-Net improves classification and calibration performance.
• Score: 0.522145960878624
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In real world datasets, particular groups are under-represented, much rarer than others, and machine learning classifiers will often preform worse on under-represented populations. This problem is aggravated across many domains where datasets are class imbalanced, with a minority class far rarer than the majority class. Naive approaches to handle under-representation and class imbalance include training sub-population specific classifiers that handle class imbalance or training a global classifier that overlooks sub-population disparities and aims to achieve high overall accuracy by handling class imbalance. In this study, we find that these approaches are vulnerable in class imbalanced datasets with minority sub-populations. We introduced Fair-Net, a branched multitask neural network architecture that improves both classification accuracy and probability calibration across identifiable sub-populations in class imbalanced datasets. Fair-Nets is a straightforward extension to the output layer and error function of a network, so can be incorporated in far more complex architectures. Empirical studies with three real world benchmark datasets demonstrate that Fair-Net improves classification and calibration performance, substantially reducing performance disparity between gender and racial sub-populations.

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