Removing Algorithmic Discrimination (With Minimal Individual Error)
This work addresses algorithmic fairness for groups affected by discrimination, offering incremental improvements in balancing fairness and accuracy.
The paper tackles the problem of correcting group discrimination in score functions while minimizing individual error, presenting analytical solutions for two populations and approximate linear programming solutions for multiple populations.
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.