Addressing Fairness, Bias and Class Imbalance in Machine Learning: the FBI-loss

TL;DR

This paper introduces FBI-loss, a unified loss function designed to simultaneously address fairness, bias, and class imbalance in machine learning, providing a comprehensive approach to improve autonomous decision-making systems.

Contribution

The paper proposes a novel unified loss correction method that formalizes and addresses fairness, biases, and imbalances as different expressions of unbalance in data.

Findings

FBI-loss effectively handles fairness, bias, and class imbalance issues.

Empirical results show competitive performance against specialized solutions.

The approach is validated on real-world benchmarks and synthetic datasets.

Abstract

Resilience to class imbalance and confounding biases, together with the assurance of fairness guarantees are highly desirable properties of autonomous decision-making systems with real-life impact. Many different targeted solutions have been proposed to address separately these three problems, however a unifying perspective seems to be missing. With this work, we provide a general formalization, showing that they are different expressions of unbalance. Following this intuition, we formulate a unified loss correction to address issues related to Fairness, Biases and Imbalances (FBI-loss). The correction capabilities of the proposed approach are assessed on three real-world benchmarks, each associated to one of the issues under consideration, and on a family of synthetic data in order to better investigate the effectiveness of our loss on tasks with different complexities. The empirical results highlight that the flexible formulation of the FBI-loss leads also to competitive performances with respect to literature solutions specialised for the single problems.