Whither Bias Goes, I Will Go: An Integrative, Systematic Review of Algorithmic Bias Mitigation
Louis Hickman, Christopher Huynh, Jessica Gass, Brandon Booth, Jason, Kuruzovich, Louis Tay

TL;DR
This paper systematically reviews the sources, definitions, legal considerations, and mitigation methods of algorithmic bias in machine learning assessments used for personnel selection, proposing an integrative four-stage framework.
Contribution
It introduces a comprehensive four-stage model of ML assessment development and bias mitigation, integrating insights from multiple disciplines and identifying gaps for future research.
Findings
Bias can originate at data generation, training, testing, and deployment stages.
Legal and fairness considerations vary across US and European contexts.
Effective bias mitigation methods are identified and aligned with legal requirements.
Abstract
Machine learning (ML) models are increasingly used for personnel assessment and selection (e.g., resume screeners, automatically scored interviews). However, concerns have been raised throughout society that ML assessments may be biased and perpetuate or exacerbate inequality. Although organizational researchers have begun investigating ML assessments from traditional psychometric and legal perspectives, there is a need to understand, clarify, and integrate fairness operationalizations and algorithmic bias mitigation methods from the computer science, data science, and organizational research literatures. We present a four-stage model of developing ML assessments and applying bias mitigation methods, including 1) generating the training data, 2) training the model, 3) testing the model, and 4) deploying the model. When introducing the four-stage model, we describe potential sources of…
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