Reducing AI bias in recruitment and selection: an integrative grounded approach

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dc.citation.volumeLatest Articles
dc.contributor.authorSoleimani M
dc.contributor.authorIntezari A
dc.contributor.authorArrowsmith J
dc.contributor.authorPauleen DJ
dc.contributor.authorTaskin N
dc.date.accessioned2025-04-08T02:35:42Z
dc.date.available2025-04-08T02:35:42Z
dc.date.issued2025-03-20
dc.description.abstractArtificial Intelligence (AI) is transforming business domains such as operations, marketing, risk, and financial management. However, its integration into Human Resource Management (HRM) poses challenges, particularly in recruitment, where AI influences work dynamics and decision-making. This study, using a grounded theory approach, interviewed 39 HR professionals and AI developers to explore potential biases in AI-Recruitment Systems (AIRS) and identify mitigation techniques. Findings highlight a critical gap: the HR profession’s need to embrace both technical skills and nuanced people-focused competencies to collaborate effectively with AI developers and drive informed discussions on the scope of AI’s role in recruitment and selection. This research integrates Gibson’s direct perception theory and Gregory’s indirect perception theory, combining psychological, information systems, and HRM perspectives to offer insights into decision-making biases in AI. A framework is proposed to clarify decision-making biases and guide the development of robust protocols for AI in HR, with a focus on ethical oversight and regulatory needs. This research contributes to AI-based HR decision-making literature by exploring the intersection of cognitive bias and AI-augmented decisions in recruitment and selection. It offers practical insights for HR professionals and AI developers on how collaboration and perception can improve the fairness and effectiveness of AIRS-aided decisions.
dc.description.confidentialfalse
dc.format.pagination1-36
dc.identifier.citationSoleimani M, Intezari A, Arrowsmith J, Pauleen DJ, Taskin N. (2025). Reducing AI bias in recruitment and selection: an integrative grounded approach. International Journal of Human Resource Management. Latest Articles. (pp. 1-36).
dc.identifier.doi10.1080/09585192.2025.2480617
dc.identifier.eissn1466-4399
dc.identifier.elements-typejournal-article
dc.identifier.issn0958-5192
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/72740
dc.languageEnglish
dc.publisherTaylor and Francis Group
dc.publisher.urihttps://www.tandfonline.com/doi/full/10.1080/09585192.2025.2480617
dc.relation.isPartOfInternational Journal of Human Resource Management
dc.rights(c) 2025 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectDecision-making
dc.subjectArtificial Intelligence (AI)
dc.subjectbias mitigation
dc.subjectHuman Resource Management (HRM)
dc.subjectrecruitment and selection
dc.subjectgrounded theory
dc.titleReducing AI bias in recruitment and selection: an integrative grounded approach
dc.typeJournal article
pubs.elements-id500301
pubs.organisational-groupOther
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