Review of Mathematical frameworks for Fairness in Machine Learning

• URL: http://arxiv.org/abs/2005.13755v1
• Date: Tue, 26 May 2020 11:40:13 GMT
• Title: Review of Mathematical frameworks for Fairness in Machine Learning
• Authors: Eustasio del Barrio, Paula Gordaliza, Jean-Michel Loubes
• Abstract summary: A review of the main fairness definitions and fair learning methodologies proposed in the literature over the last years is presented. We consider how to build fair algorithms and the consequences on the degradation of their performance compared to the possibly unfair case.
• Score: 6.273722322121772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A review of the main fairness definitions and fair learning methodologies proposed in the literature over the last years is presented from a mathematical point of view. Following our independence-based approach, we consider how to build fair algorithms and the consequences on the degradation of their performance compared to the possibly unfair case. This corresponds to the price for fairness given by the criteria $\textit{statistical parity}$ or $\textit{equality of odds}$. Novel results giving the expressions of the optimal fair classifier and the optimal fair predictor (under a linear regression gaussian model) in the sense of $\textit{equality of odds}$ are presented.

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