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Investigating the Legality of Bias Mitigation Methods in the United Kingdom

Mackenzie Jorgensen, Madeleine Waller, Oana Cocarascu, Natalia Criado, Odinaldo Rodrigues, Jose Such, Elizabeth Black

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
1 Downloads (Pure)

Abstract

Algorithmic Decision-Making Systems (ADMS) 1 fairness issues have been well highlighted over the past decade [1], including some facial recognition systems struggling to identify people of color [2]. In 2021, Uber drivers filed a claim with the U.K. ’s employment tribunal for unfair dismissal resulting from automated facial recognition technology by Microsoft [3]. Bias mitigation methods have been developed to reduce discrimination from ADMS. These typically operationalize fairness notions as fairness metrics to minimize discrimination [4]. We refer to ADMS to which bias mitigation methods have been applied as “mitigated ADMS” or, in the singular, a “mitigated system.”
Original languageEnglish
Pages (from-to)87-94
Number of pages8
JournalIEEE Technology and Society Magazine
Volume42
Issue number4
DOIs
Publication statusPublished - 19 Jan 2024
Externally publishedYes

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