Whither Fair Clustering?

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Abstract

Within the relatively busy area of fair machine learning that has been dominated by classification fairness research, fairness in clustering has started to see some recent attention. In this position paper, we assess the existing work in fair clustering and observe that there are several directions that are yet to be explored, and postulate that the state-of-the-art in fair clustering has been quite parochial in outlook. We posit that widening the normative principles to target for, characterizing shortfalls where the target cannot be achieved fully, and making use of knowledge of downstream processes can significantly widen the scope of research in fair clustering research. At a time when clustering and unsupervised learning are being increasingly used to make and influence decisions that matter significantly to human lives, we believe that widening the ambit of fair clustering is of immense significance.
Original languageEnglish
Title of host publicationAI for Social Good: Harvard CRCS Workshop
Publication statusAccepted - 08 Jul 2020
EventAI for Social Good: Harvard CRCS Workshop -
Duration: 20 Jul 202021 Jul 2020
https://aiforgood2020.github.io/

Conference

ConferenceAI for Social Good
Abbreviated titleAI4SG
Period20/07/202021/07/2020
Internet address

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  • Cite this

    Padmanabhan, D. (Accepted/In press). Whither Fair Clustering? In AI for Social Good: Harvard CRCS Workshop