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Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference”

In this paper, we analyze two key claims offered by recruitment AI companies in relation to the development and deployment of AI-powered HR tools: (1) recruitment AI can objectively assess candidates by removing gender and race from their systems, and (2) this removal of gender and race will make re...

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Autores principales: Drage, Eleanor, Mackereth, Kerry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550152/
https://www.ncbi.nlm.nih.gov/pubmed/36246553
http://dx.doi.org/10.1007/s13347-022-00543-1
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author Drage, Eleanor
Mackereth, Kerry
author_facet Drage, Eleanor
Mackereth, Kerry
author_sort Drage, Eleanor
collection PubMed
description In this paper, we analyze two key claims offered by recruitment AI companies in relation to the development and deployment of AI-powered HR tools: (1) recruitment AI can objectively assess candidates by removing gender and race from their systems, and (2) this removal of gender and race will make recruitment fairer, help customers attain their DEI goals, and lay the foundations for a truly meritocratic culture to thrive within an organization. We argue that these claims are misleading for four reasons: First, attempts to “strip” gender and race from AI systems often misunderstand what gender and race are, casting them as isolatable attributes rather than broader systems of power. Second, the attempted outsourcing of “diversity work” to AI-powered hiring tools may unintentionally entrench cultures of inequality and discrimination by failing to address the systemic problems within organizations. Third, AI hiring tools’ supposedly neutral assessment of candidates’ traits belie the power relationship between the observer and the observed. Specifically, the racialized history of character analysis and its associated processes of classification and categorization play into longer histories of taxonomical sorting and reflect the current demands and desires of the job market, even when not explicitly conducted along the lines of gender and race. Fourth, recruitment AI tools help produce the “ideal candidate” that they supposedly identify through by constructing associations between words and people’s bodies. From these four conclusions outlined above, we offer three key recommendations to AI HR firms, their customers, and policy makers going forward.
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spelling pubmed-95501522022-10-11 Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference” Drage, Eleanor Mackereth, Kerry Philos Technol Research Article In this paper, we analyze two key claims offered by recruitment AI companies in relation to the development and deployment of AI-powered HR tools: (1) recruitment AI can objectively assess candidates by removing gender and race from their systems, and (2) this removal of gender and race will make recruitment fairer, help customers attain their DEI goals, and lay the foundations for a truly meritocratic culture to thrive within an organization. We argue that these claims are misleading for four reasons: First, attempts to “strip” gender and race from AI systems often misunderstand what gender and race are, casting them as isolatable attributes rather than broader systems of power. Second, the attempted outsourcing of “diversity work” to AI-powered hiring tools may unintentionally entrench cultures of inequality and discrimination by failing to address the systemic problems within organizations. Third, AI hiring tools’ supposedly neutral assessment of candidates’ traits belie the power relationship between the observer and the observed. Specifically, the racialized history of character analysis and its associated processes of classification and categorization play into longer histories of taxonomical sorting and reflect the current demands and desires of the job market, even when not explicitly conducted along the lines of gender and race. Fourth, recruitment AI tools help produce the “ideal candidate” that they supposedly identify through by constructing associations between words and people’s bodies. From these four conclusions outlined above, we offer three key recommendations to AI HR firms, their customers, and policy makers going forward. Springer Netherlands 2022-10-10 2022 /pmc/articles/PMC9550152/ /pubmed/36246553 http://dx.doi.org/10.1007/s13347-022-00543-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Article
Drage, Eleanor
Mackereth, Kerry
Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference”
title Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference”
title_full Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference”
title_fullStr Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference”
title_full_unstemmed Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference”
title_short Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference”
title_sort does ai debias recruitment? race, gender, and ai’s “eradication of difference”
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550152/
https://www.ncbi.nlm.nih.gov/pubmed/36246553
http://dx.doi.org/10.1007/s13347-022-00543-1
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