EURO 2024 Copenhagen
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1602. An Optimization-Based Order-and-Cut Approach for Fair Clustering of Data Sets

Invited abstract in session TC-27: Mathematical Optimization for Trustworthy Machine Learning, stream Mathematical Optimization for XAI.

Tuesday, 12:30-14:00
Room: 047 (building: 208)

Authors (first author is the speaker)

1. Hrayer Aprahamian
Industrial and Systems Engineering, Texas A&M University

Abstract

Machine learning algorithms have been increasingly integrated into applications that significantly affect human lives. This has spurred interest in designing algorithms that train machine learning models to minimize the training error while imposing a certain level of fairness. In this paper, we consider the problem of fair clustering of datasets. In particular, given a set of items, each associated with a vector of non-sensitive attribute values and a categorical sensitive attribute, our goal is to find a clustering of the items that minimizes the loss (i.e., the clustering objective) function while imposing fairness measured by Rényi correlation. We propose an efficient and scalable in-processing algorithm, driven by findings from the field of combinatorial optimization, that heuristically solves the underlying optimization problem and allows for regulating the trade-off between clustering quality and fairness. The approach does not restrict the analysis to a specific loss function but instead considers a more general form that satisfies certain properties. This broadens the scope of the algorithm's applicability. We demonstrate the effectiveness of the algorithm for the specific case of k-means clustering, as it is one of the most extensively studied and widely adopted clustering schemes. Our numerical experiments reveal that the proposed algorithm outperforms existing methods by providing a more effective mechanism to regulate the trade-off between loss and fairness.

Keywords

Status: accepted

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