Exploring Rawlsian fairness for k-means clustering

Stanley Simoes, Deepak P.*, Muiris MacCarthaigh

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

We conduct an exploratory study that looks at incorporating John Rawls' ideas on fairness into existing unsupervised machine learning algorithms. Our focus is on the task of clustering, specifically the k-means clustering algorithm. To the best of our knowledge, this is the first work that uses Rawlsian ideas in clustering. Towards this, we attempt to develop a postprocessing technique i.e., one that operates on the cluster assignment generated by the standard k-means clustering algorithm. Our technique perturbs this assignment over a number of iterations to make it fairer according to Rawls' difference principle while minimally affecting the overall utility. As the first step, we consider two simple perturbation operators -- R1 and R2 -- that reassign examples in a given cluster assignment to new clusters; R1 assigning a single example to a new cluster, and R2 a pair of examples to new clusters. Our experiments on a sample of the Adult dataset demonstrate that both operators make meaningful perturbations in the cluster assignment towards incorporating Rawls' difference principle, with R2 being more efficient than R1 in terms of the number of iterations. However, we observe that there is still a need to design operators that make significantly better perturbations. Nevertheless, both operators provide good baselines for designing and comparing any future operator, and we hope our findings would aid future work in this direction.
Original languageEnglish
Title of host publicationResponsible Data Science. Select Proceedings of ICDSE 2021
EditorsJimson Mathew, G. Santosh Kumar, Deepak P., Joemon M. Jose
Place of PublicationSingapore
PublisherSpringer
Pages47-59
Number of pages12
Volume940
ISBN (Electronic)9789811944536
ISBN (Print)9789811944529
DOIs
Publication statusPublished - 15 Nov 2022

Publication series

NameLecture Notes in Electrical Engineering
PublisherSpringer
Volume940
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

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