Too Relaxed to Be Fair

2020-01-01 · ICML 2020 1 · Michael Lohaus, Michaël Perrot, Ulrike Von Luxburg

We address the problem of classification under fairness constraints. Given a notion of fairness, the goal is to learn a classifier that is not discriminatory against a group of individuals. In the literature, this problem is often formulated as a constrained optimization problem and solved using relaxations of the fairness constraints. We show that many existing relaxations are unsatisfactory: even if a model satisfies the relaxed constraint, it can be surprisingly unfair. We propose a principled framework to solve this problem. This new approach uses a strongly convex formulation and comes with theoretical guarantees on the fairness of its solution. In practice, we show that this method gives promising results on real data.

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mlohaus/SearchFair 공식 구현

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Fairness

Geometry of Relaxed Fair Regression: A Unified Framework for Aware and Unaware Settings

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Autonomous Driving BIG-bench Machine Learning Classification Fairness+2

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Oblivious Data for Fairness with Kernels

2020-02-07 · Steffen Grünewälder, Azadeh Khaleghi

We investigate the problem of algorithmic fairness in the case where sensitive and non-sensitive features are available and one aims to generate new, `oblivious', features that closely approximate the non-sensitive featu…

Fairness

Mean Parity Fair Regression in RKHS

2023-02-21 · Shaokui Wei, Jiayin Liu, Bing Li, Hongyuan Zha

We study the fair regression problem under the notion of Mean Parity (MP) fairness, which requires the conditional mean of the learned function output to be constant with respect to the sensitive attributes. We address t…

Fairness regression