Representativity Fairness in Clustering

Deepak P. Padmanabhan, Savitha Sam Abraham

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

Abstract

In this paper, we develop a novel notion of fairness in clustering, called representativity fairness. Representativity fairness is motivated by the need to alleviate disparity across objects' proximity to their assigned cluster representatives, to aid fairer decision making.
We develop a new clustering formulation, RFKM, that targets to optimize for representativity fairness along with clustering quality.
Original languageEnglish
Title of host publication12TH ACM Web Science Conference 2020: Proceedings
PublisherAssociation for Computing Machinery (ACM)
Pages202-211
Number of pages10
DOIs
Publication statusPublished - Jul 2020
EventACM WebSci 2020 - Southampton, United Kingdom
Duration: 06 Jul 202010 Jul 2020
https://websci20.webscience.org/

Conference

ConferenceACM WebSci 2020
CountryUnited Kingdom
CitySouthampton
Period06/07/202010/07/2020
Internet address

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  • Cite this

    Padmanabhan, D. P., & Sam Abraham, S. (2020). Representativity Fairness in Clustering. In 12TH ACM Web Science Conference 2020: Proceedings (pp. 202-211). Association for Computing Machinery (ACM). https://doi.org/10.1145/3394231.3397910