调查英国减少偏见方法的合法性

IF 2.1 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Mackenzie Jorgensen;Madeleine Waller;Oana Cocarascu;Natalia Criado;Odinaldo Rodrigues;Jose Such;Elizabeth Black
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引用次数: 0

摘要

过去十年来,算法决策系统(ADMS)1 的公平性问题一直备受关注[1],其中包括一些面部识别系统在识别有色人种方面的困难[2]。2021 年,Uber 司机向英国就业法庭提起诉讼,指控微软公司的自动人脸识别技术造成了不公平解雇[3]。为了减少 ADMS 带来的歧视,人们开发了减少偏见的方法。这些方法通常将公平概念作为公平指标来操作,以尽量减少歧视[4]。我们将采用了偏差缓解方法的 ADMS 称为 "缓解 ADMS",或单称 "缓解系统"。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigating the Legality of Bias Mitigation Methods in the United Kingdom
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.”
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来源期刊
IEEE Technology and Society Magazine
IEEE Technology and Society Magazine 工程技术-工程:电子与电气
CiteScore
3.00
自引率
13.60%
发文量
72
审稿时长
>12 weeks
期刊介绍: IEEE Technology and Society Magazine invites feature articles (refereed), special articles, and commentaries on topics within the scope of the IEEE Society on Social Implications of Technology, in the broad areas of social implications of electrotechnology, history of electrotechnology, and engineering ethics.
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