Related papers: Fair Interpretable Learning via Correction Vectors

Fair Interpretable Learning via Correction Vectors

URL: http://arxiv.org/abs/2201.06343v1
Date: Mon, 17 Jan 2022 10:59:33 GMT
Title: Fair Interpretable Learning via Correction Vectors
Authors: Mattia Cerrato and Marius K\"oppel and Alexander Segner and Stefan Kramer
Abstract summary: We propose a new framework for fair representation learning centered around the learning of "correction vectors" The corrections are then simply summed up to the original features, and can therefore be analyzed as an explicit penalty or bonus to each feature. We show experimentally that a fair representation learning problem constrained in such a way does not impact performance.
Score: 68.29997072804537
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector which is independent of sensitive information. Various "representation debiasing" techniques have been proposed in the literature. However, as neural networks are inherently opaque, these methods are hard to comprehend, which limits their usefulness. We propose a new framework for fair representation learning which is centered around the learning of "correction vectors", which have the same dimensionality as the given data vectors. The corrections are then simply summed up to the original features, and can therefore be analyzed as an explicit penalty or bonus to each feature. We show experimentally that a fair representation learning problem constrained in such a way does not impact performance.

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