Representativity Fairness in Clustering

Deepak P. Padmanabhan; Savitha Sam Abraham

doi:10.1145/3394231.3397910

Representativity Fairness in Clustering

Deepak P. Padmanabhan
, Savitha Sam Abraham

School of Electronics, Electrical Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

229 Downloads (Pure)

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 language	English
Title of host publication	12TH ACM Web Science Conference 2020: Proceedings
Publisher	Association for Computing Machinery
Pages	202-211
Number of pages	10
DOIs	https://doi.org/10.1145/3394231.3397910
Publication status	Published - Jul 2020
Event	ACM WebSci 2020 - Southampton, United Kingdom Duration: 06 Jul 2020 → 10 Jul 2020 https://websci20.webscience.org/

Conference

Conference	ACM WebSci 2020
Country/Territory	United Kingdom
City	Southampton
Period	06/07/2020 → 10/07/2020
Internet address	https://websci20.webscience.org/

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10.1145/3394231.3397910Licence: Unspecified

Representativity Fairness in Clustering
Copyright 2020 ACM.
Final published version, 725 KBLicence: Unspecified

Cite this

@inproceedings{08a4b37503ea45b38238a0b726aafd40,

title = "Representativity Fairness in Clustering",

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.",

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AB - 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.

U2 - 10.1145/3394231.3397910

DO - 10.1145/3394231.3397910

M3 - Conference contribution

SP - 202

EP - 211

BT - 12TH ACM Web Science Conference 2020: Proceedings

PB - Association for Computing Machinery

T2 - ACM WebSci 2020

Y2 - 6 July 2020 through 10 July 2020

ER -

Representativity Fairness in Clustering

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Conference

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