9卷 第3期
Facul Kong Shatin, NewTerritories, HongKong, 999077, China
纸质出版：2025-09-30
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(2025). Toward a Fairer Future:Understanding and Addressing Algorithmic Bias in Hiring. 商事法律评论（英文版）, 9(3), 54-59.
Rui Zhong.Toward a Fairer Future:Understanding and Addressing Algorithmic Bias in Hiring[J].Journal of Commercial Law Review,2025,09(03):54-59.
(2025). Toward a Fairer Future:Understanding and Addressing Algorithmic Bias in Hiring. 商事法律评论（英文版）, 9(3), 54-59. DOI： 10.47297/wspjclrWSP2516-249711.20250903.
Rui Zhong.Toward a Fairer Future:Understanding and Addressing Algorithmic Bias in Hiring[J].Journal of Commercial Law Review,2025,09(03):54-59. DOI： 10.47297/wspjclrWSP2516-249711.20250903.
AI-driven hiring systems have gradually made a difference instead of traditional hiring patterns. However
such hiring algorithms with a mask of neutrality may lead to algorithmic bias
damaging equal employment rights
which is defined as the outputs of AI algorithms benefit or disadvantage certain individuals or groups more than others without justifications for such unequal impacts. The bias can stem from two stages of algorithm design including data collection and modelling. In the stage of data collection
historical data collected containing human bias and reflecting societal inequity will perpetuate the bias
and non-representative data collected violating diversity or completeness of data will also result in the bias. In the stage of modelling
irrelevant or redundant features selected like sensitive attributes and proxy attributes closely related to sensitive attributes will introduce the bias
and opaque black-box system with less explainability may contain unknown bias. Technically
the bias can be mitigated through debiasing datasets involved in diversifying data and reweighting and resampling in preprocess
introducing fairness metrics in modelling
and increasing transparency. Regarding with legal governance
the framework of AI fairness reporting and auditing should be constructed to identify bias and achieve transparency and liability of bias should be regulated to clarify who shall be liable and why shall be liable.
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