人工智能在人力资源管理中的应用：经验主义的局限

Authors / Janine Berg, Hannah Johnston © International Labour Organization 2025 Attribution 4.0 International (CC BY 4.0) This work is licensed under the Creative Commons Attribution 4.0 International. See:https://creativecommons.org/licenses/by/4.0/. The user is allowed to reuse, share (copy and redistrib-ute), adapt (remix, transform and build upon the original work) as detailed in the licence. Theuser must clearly credit the ILO as the source of the material and indicate if changes were made Attribution– The user must indicate if changes were made and must cite the work as follows:Berg, J., Johnston, H.AI in human resource management: The limits of empiricism. ILO Working Paper154. Geneva: International Labour Office, 2025.© ILO. Translations– In case of a translation of this work, the following disclaimer must be addedalong with the attribution:This is a translation of a copyrighted work of the International LabourOrganization (ILO). This translation has not been prepared, reviewed or endorsed by the ILO and should Adaptations– In case of an adaptation of this work, the following disclaimer must be addedalong with the attribution:This is an adaptation of a copyrighted work of the International LabourOrganization (ILO). This adaptation has not been prepared, reviewed or endorsed by the ILO and should Third-party materials– This Creative Commons licence does not apply to non-ILO copyright ma-terials included in this publication. If the material is attributed to a third party, the user of such Any dispute arising under this licence that cannot be settled amicably shall be referred to arbitra-tion in accordance with the Arbitration Rules of the United Nations Commission on International For details on rights and licensing, contact:rights@ilo.org. For details on ILO publications anddigital products, visit:www.ilo.org/publns. ISBN 9789220428610 (print), ISBN 9789220428627 (web PDF), ISBN 9789220428634 (epub), ISBN9789220428641 (html). ISSN 2708-3438 (print), ISSN 2708-3446 (digital) https://doi.org/10.54394/NMSH7611 The designations employed in ILO publications, which are in conformity with United Nationspractice, and the presentation of material therein do not imply the expression of any opinion or of its authorities, or concerning the delimitation of its frontiers or boundaries. See:www.ilo.org/disclaimer. The opinions and views expressed in this publication are those of the author(s) and do not nec-essarily reflect the opinions, views or policies of the ILO. Reference to names of firms and commercial products and processes does not imply their en-dorsement by the ILO, and any failure to mention a particular firm, commercial product or pro- Information on ILO publications and digital products can be found at:www.ilo.org/research-and-publications ILO Working Papers summarize the results of ILO research in progress, and seek to stimulatediscussion of a range of issues related to the world of work. Comments on this ILO Working Paper Authorization for publication: Caroline Fredrickson, Director, Research Department ILO Working Papers can be found at:www.ilo.org/research-and-publications/working-papers Suggested citation:Berg, J., Johnston, H. 2025.AI in human resource management: The limits of empiricism, ILO Working Paper 154 (Geneva, ILO).https://doi.org/10.54394/NMSH7611 Abstract The rapid integration of artificial intelligence (AI) into Human Resource Management (HRM) istransforming how organizations recruit, manage, and evaluate their workforces. While propo-nents champion AI as a means to enhance efficiency, reduce bias, and align HR practices withstrategic business goals, this paper argues that such optimism is misplaced. Drawing on a critical Central to the analysis is a three-parameter framework for assessing AI tools: theirobjective, thedatathey rely upon, and how they areprogrammed. The paper shows that across HR functions,AI systems frequently operationalize reductive or poorly aligned objectives, rely on low-qualityor biased data, and are programmed in non-transparent ways that undermine their usefulness.These structural shortcomings not only undermine the effectiveness of AI systems but also in- Keywords: artificial intelligence, human resource management, data analytics, algorithmic man-agement About the authors Janine Bergis Senior Economist and Head of the Effective Labour Institutions Unit in the ResearchDepartment of the ILO. Since joining the ILO in 2002, she has conducted research on the econom-ic and social effects of labour laws as well as provided technical assistance on policies for gen- Hannah Johnstonis an Assistant Professor in the School of Human Resources Managementat York University in Toronto, Canada, specializing on the digitalization of work. Prior to joiningYork, Hannah was a postdoctoral fellow at Northeastern University in Boston and also workedprofessionally at the International Labour Organization. Hannah has a longstanding interest in Table of