Learning to Generate Fair Clusters from Demonstrations

TL;DR

This paper introduces a method to infer fairness constraints from expert demonstrations and generate fair, interpretable clusters, addressing the challenge of incomplete fairness specifications in clustering tasks.

Contribution

It proposes an algorithm to identify fairness metrics from demonstrations and extends it with a greedy clustering method for new fairness metrics, with theoretical analysis and interpretability focus.

Findings

Successfully identifies underlying fairness constraints from limited demonstrations

Effectively generates fair and interpretable clusters on real-world datasets

Demonstrates rapid and accurate inference of fairness metrics

Abstract

Fair clustering is the process of grouping similar entities together, while satisfying a mathematically well-defined fairness metric as a constraint. Due to the practical challenges in precise model specification, the prescribed fairness constraints are often incomplete and act as proxies to the intended fairness requirement, leading to biased outcomes when the system is deployed. We examine how to identify the intended fairness constraint for a problem based on limited demonstrations from an expert. Each demonstration is a clustering over a subset of the data. We present an algorithm to identify the fairness metric from demonstrations and generate clusters using existing off-the-shelf clustering techniques, and analyze its theoretical properties. To extend our approach to novel fairness metrics for which clustering algorithms do not currently exist, we present a greedy method for clustering. Additionally, we investigate how to generate interpretable solutions using our approach. Empirical evaluation on three real-world datasets demonstrates the effectiveness of our approach in quickly identifying the underlying fairness and interpretability constraints, which are then used to generate fair and interpretable clusters.