A Tutorial and Resources for Fair Clustering

Fair Clustering & Unsupervised Learning

The goal of this tutorial is to introduce a wide audience interested in algorithmic fairness to the nascent research area of fair clustering. Specifically, we wish to present a variety of fairness notions used in the context of clustering, argue about the necessity of each of those through corresponding applications, discuss the relationships between different notions, sketch the algorithmic ideas that were developed in order to address the corresponding computational problems, and finally share our thoughts about the future of research in algorithmic fairness. By the end of the tutorial, the audience will have achieved a significant level of familiarity with multiple definitions of fairness in the unsupervised learning context, and we hope that researchers will use these ideas in contexts both within and adjacent to the clustering context, in both industrial and academic applications.


AAAI 2022 Schedule — February 2022 — YouTube Playlist

Section Speaker Duration Slides Video
Introduction to Clustering Paradigms Seyed Esmaeili 30 minutes + 5 minutes QnA pdf, pptx YouTube
Notions of Demographic Fairness Marina Knittel and Leonidas Tsepenekas 1 hour + 5 minutes QnA pdf, pptx YouTube (#1), YouTube (#2)
Break 15 minutes
Notions of Individual Fairness Leonidas Tsepenekas 45 minutes + 5 minutes QnA pdf, pptx YouTube
Algorithmic Aspects of Fair Clustering Seyed Esmaeili 20 minutes + 5 minutes QnA pdf, pptx YouTube
Risks of Use and Advice for Responsible, Interdisciplinary Work Brian Brubach 45 minutes + 5 minutes QnA pdf, pptx YouTube

About Us

This is ongoing research and work led by the following team; if you are interested in joining, please feel free to reach out!

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