Toward a Fairer Future:Understanding and Addressing Algorithmic Bias in Hiring

Rui Zhong

doi:10.47297/wspjclrWSP2516-249711.20250903

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Toward a Fairer Future:Understanding and Addressing Algorithmic Bias in Hiring

Volume 9, Issue 3

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Toward a Fairer Future:Understanding and Addressing Algorithmic Bias in Hiring
Toward a Fairer Future:Understanding and Addressing Algorithmic Bias in Hiring
Facul Kong Shatin, NewTerritories, HongKong, 999077, China
Vol. 9, Issue 3, Pages: 54-59(2025)
Published：30 September 2025
DOI：10.47297/wspjclrWSP2516-249711.20250903
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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：

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.

摘要

Abstract

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.

关键词

Keywords

references

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