CONFAIR: Configurable and Interpretable Algorithmic Fairness

TL;DR

CONFAIR introduces a flexible, interpretable framework for algorithmic fairness that allows user-defined constraints and feature importance estimation, addressing limitations of existing fairness methods.

Contribution

It proposes a novel, configurable approach to fairness that incorporates user constraints and enhances interpretability in decision-making algorithms.

Findings

Effective on real-world datasets

Supports multiple fairness criteria

Provides interpretable feature importance

Abstract

The rapid growth of data in the recent years has led to the development of complex learning algorithms that are often used to make decisions in real world. While the positive impact of the algorithms has been tremendous, there is a need to mitigate any bias arising from either training samples or implicit assumptions made about the data samples. This need becomes critical when algorithms are used in automated decision making systems that can hugely impact people's lives. Many approaches have been proposed to make learning algorithms fair by detecting and mitigating bias in different stages of optimization. However, due to a lack of a universal definition of fairness, these algorithms optimize for a particular interpretation of fairness which makes them limited for real world use. Moreover, an underlying assumption that is common to all algorithms is the apparent equivalence of achieving fairness and removing bias. In other words, there is no user defined criteria that can be incorporated into the optimization procedure for producing a fair algorithm. Motivated by these shortcomings of existing methods, we propose the CONFAIR procedure that produces a fair algorithm by incorporating user constraints into the optimization procedure. Furthermore, we make the process interpretable by estimating the most predictive features from data. We demonstrate the efficacy of our approach on several real world datasets using different fairness criteria.