Obtaining fairness using optimal transport theory

Abstract

Statistical algorithms are usually helping in making decisions in many aspects of our lives. But, how do we know if these algorithms are biased and commit unfair discrimination of a particular group of people, typically a minority? \textit{Fairness} is generally studied in a probabilistic framework where it is assumed that there exists a protected variable, whose use as an input of the algorithm may imply discrimination. There are different definitions of Fairness in the literature. In this paper we focus on two of them which are called Disparate Impact (DI) and Balanced Error Rate (BER). Both are based on the outcome of the algorithm across the different groups determined by the protected variable. The relationship between these two notions is also studied. The goals of this paper are to detect when a binary classification rule lacks fairness and to try to fight against the potential discrimination attributable to it. This can be done by modifying either the classifiers or the data itself. Our work falls into the second category and modifies the input data using optimal transport theory.

References in corpus (2)

• Fast Computation of Wasserstein Barycenters
• Characterization of barycenters in the Wasserstein space by averaging optimal transport maps

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• Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey
• All of the Fairness for Edge Prediction with Optimal Transport
• A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set
• Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees
• Confidence Intervals for Testing Disparate Impact in Fair Learning
• Fairness with Continuous Optimal Transport
• Fairness criteria through the lens of directed acyclic graphical models
• A Central Limit Theorem for $L_p$ transportation cost with applications to Fairness Assessment in Machine Learning
• Fairness-aware Summarization for Justified Decision-Making
• Attraction-Repulsion clustering with applications to fairness
• Fairness constraints can help exact inference in structured prediction