Tuning Fairness by Balancing Target Labels

TL;DR

This paper introduces a probabilistic framework for fairness in machine learning that balances target labels, unifying various fairness notions and enabling direct control over fairness levels without complex optimization.

Contribution

It proposes a novel latent target output approach that unifies multiple fairness definitions and simplifies fairness control in probabilistic models.

Findings

Framework unifies Demographic Parity and Equality of Opportunity.

Allows direct fairness level control by varying target rates.

Avoids unstable constrained optimization procedures.

Abstract

The issue of fairness in machine learning models has recently attracted a lot of attention as ensuring it will ensure continued confidence of the general public in the deployment of machine learning systems. We focus on mitigating the harm incurred by a biased machine learning system that offers better outputs (e.g. loans, job interviews) for certain groups than for others. We show that bias in the output can naturally be controlled in probabilistic models by introducing a latent target output. This formulation has several advantages: first, it is a unified framework for several notions of group fairness such as Demographic Parity and Equality of Opportunity; second, it is expressed as a marginalisation instead of a constrained problem; and third, it allows the encoding of our knowledge of what unbiased outputs should be. Practically, the second allows us to avoid unstable constrained optimisation procedures and to reuse off-the-shelf toolboxes. The latter translates to the ability to control the level of fairness by directly varying fairness target rates. In contrast, existing approaches rely on intermediate, arguably unintuitive, control parameters such as covariance thresholds.