Removing Algorithmic Discrimination (With Minimal Individual Error)

TL;DR

This paper proposes methods to reduce group discrimination in scoring systems while keeping individual errors minimal, using analytical solutions for two groups and linear programming for multiple groups.

Contribution

It introduces an analytical solution for two populations and a linear programming approach for multiple populations to minimize discrimination with minimal individual error.

Findings

Analytical solution for two populations with uniform bonus-malus.

Linear programming method for multiple populations.

Effective minimization of discrimination within error bounds.

Abstract

We address the problem of correcting group discriminations within a score function, while minimizing the individual error. Each group is described by a probability density function on the set of profiles. We first solve the problem analytically in the case of two populations, with a uniform bonus-malus on the zones where each population is a majority. We then address the general case of n populations, where the entanglement of populations does not allow a similar analytical solution. We show that an approximate solution with an arbitrarily high level of precision can be computed with linear programming. Finally, we address the inverse problem where the error should not go beyond a certain value and we seek to minimize the discrimination.