Exploring Gender Bias and Algorithm Transparency: Ethical Considerations of AI in HRM
DOI:
https://doi.org/10.53469/jtpms.2024.04(03).06Keywords:
Artificial intelligence (AI), Ethical implications, Gender bias, Algorithm transparency, HRM policy, Regulatory frameworksAbstract
Opportunities and challenges are introduced by the integration of Artificial Intelligence (AI) into Human Resource Management (HRM). The paragraph discusses the ethical implications of AI applications in HRM, focusing on gender bias and algorithm transparency. It explores how AI-driven decision-making in HRM perpetuates gender bias, the importance of transparent algorithms for trust and accountability, and the role of regulatory frameworks in safeguarding ethical standards. The paper aims to provide a comprehensive analysis of the ethical landscape of AI in HRM and offers policy recommendations to mitigate bias and enhance transparency.
References
Ainsworth, S. and Pekarek, A. (2022). Gender in Human Resources: Hiding in plain sight. Human Resource Management Journal, 32(4). doi:https://doi.org/10.1111/1748-8583.12437
Alicia de Manuel, Janet Delgado Rodríguez, Iris Parra Jounou, Txetxu Ausín, Casacuberta, D., Cruz, M., Guersenzvaig, A., Moyano, C., Rodríguez-Arias, D., Rueda, J. and Puyol, A. (2023). Ethical assessments and mitigation strategies for biases in AI-systems used during the COVID-19 pandemic. Big Data & Society, 10(1). doi:https://doi.org/10.1177/20539517231179199.
Balasubramaniam, N., Kauppinen, M., Rannisto, A., Hiekkanen, K. and Kujala, S. (2023). Transparency and Explainability of AI Systems: From Ethical Guidelines to Requirements. Information and Software Technology, 159, p.107197. doi:https://doi.org/10.1016/j.infsof.2023.107197.
Blackman, R. and Ammanath, B. (2022). Building Transparency into AI Projects. [online] Harvard Business Review. Available at: https://hbr.org/2022/06/building-transparency-into-ai-projects .
Bujold, A., Roberge‐Maltais, I., Parent-Rocheleau, X., Boasen, J., Sylvain Sénécal and Pierre-Majorique Léger (2023). Responsible artificial intelligence in human resources management: a review of the empirical literature. doi:https://doi.org/10.1007/s43681-023-00325-1.
Chen, Z. (2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanities and Social Sciences Communications, 10(1). doi:https://doi.org/10.1057/s41599-023-02079-x.
Delecraz, S., Eltarr, L., Becuwe, M., Bouxin, H., Boutin, N. and Oullier, O. (2022). Responsible Artificial Intelligence in Human Resources Technology: An innovative inclusive and fair by design matching algorithm for job recruitment purposes. Journal of Responsible Technology, 11, p.100041. doi:https://doi.org/10.1016/j.jrt.2022.100041.
Dennis, M.J. and Aizenberg, E. (2022). The Ethics of AI in Human Resources. Ethics and Information Technology, [online] 24(3). doi:https://doi.org/10.1007/s10676-022-09653-y.
Drage, E. and Mackereth, K. (2022). Does AI Debias Recruitment? Race, Gender, and AI’s ‘Eradication of Difference’. Philosophy & Technology, [online] 35(4). doi:https://doi.org/10.1007/s13347-022-00543-1.
Feast, J. (2019). 4 Ways to Address Gender Bias in AI. [online] Harvard Business Review. Available at: https://hbr.org/2019/11/4-ways-to-address-gender-bias-in-ai [Accessed 21 Jan. 2024].
Feast, J. (2023). How to Identify and Mitigate Gender Bias in AI. [online] Cogito. Available at: https://cogitocorp.com/blog/how-to-identify-and-mitigate-gender-bias-in-ai/ [Accessed 21 Jan. 2024].
Felzmann, H., Fosch-Villaronga, E., Lutz, C. and Tamò-Larrieux, A. (2020). Towards Transparency by Design for Artificial Intelligence. Science and Engineering Ethics, 26(6), pp.3333–3361. doi:https://doi.org/10.1007/s11948-020-00276-4.
Gartner (2023). AI in HR: How AI Is Transforming the Future of HR. [online] Gartner. Available at: https://www.gartner.com/en/human-resources/topics/artificial-intelligence-in-hr [Accessed 21 Jan. 2024].
Goldstein, J. (2023). HR and talent in the era of AI. [online] IBM Blog. Available at: https://www.ibm.com/blog/hr-and-talent-in-the-era-of-ai/ [Accessed 21 Jan. 2024].
Gupta, M., Parra, C.M. and Dennehy, D. (2021). Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter? Information Systems Frontiers, 24(5). doi:https://doi.org/10.1007/s10796-021-10156-2.
Hamou-Lhadj, A. (2021). Five best practices to improve compliance with AI regulations and standards. [online] IBM Blog. Available at: https://www.ibm.com/blog/five-best-practices-to-improve-compliance-with-ai-regulations-and-standards/ [Accessed 21 Jan. 2024].
Harbert, T. (2022). Regulations Ahead on AI. [online] www.shrm.org. Available at: https://www.shrm.org/topics-tools/news/all-things-work/regulations-ahead-ai [Accessed 21 Jan. 2024].
Hunkenschroer, A.L. and Kriebitz, A. (2022). Is AI recruiting (un)ethical? A human rights perspective on the use of AI for hiring. AI and Ethics, 3(1). doi:https://doi.org/10.1007/s43681-022-00166-4.
ISACA (2022). Developing an Artificial Intelligence Governance Framework. [online] ISACA. Available at: https://www.isaca.org/resources/news-and-trends/newsletters/atisaca/2022/volume-38/developing-an-artificial-intelligence-governance-framework [Accessed 21 Jan. 2024].
Jakob Mökander (2023). Auditing of AI: Legal, Ethical and Technical Approaches. Digital Society, 2(3). doi:https://doi.org/10.1007/s44206-023-00074-y.
Jatobá, M., Santos, J., Gutierriz, I., Moscon, D., Fernandes, P.O. and Teixeira, J.P. (2019). Evolution of Artificial Intelligence Research in Human Resources. Procedia Computer Science, [online] 164, pp.137–142. doi:https://doi.org/10.1016/j.procs.2019.12.165.
Lawton, G. (2023). AI transparency: What is it and why do we need it? [online] SearchCIO. Available at: https://www.techtarget.com/searchcio/tip/AI-transparency-What-is-it-and-why-do-we-need-it [Accessed 21 Jan. 2024].
Lu, J., Lee, D., Tae Jeong Kim and Danks, D. (2020). Good Explanation for Algorithmic Transparency. National Conference on Artificial Intelligence. doi:https://doi.org/10.1145/3375627.3375821.
Madgavkar, A. (2021). A conversation on artificial intelligence and gender bias | McKinsey. [online] www.mckinsey.com. Available at: https://www.mckinsey.com/featured-insights/asia-pacific/a-conversation-on-artificial-intelligence-and-gender-bias [Accessed 21 Jan. 2024].
Manasi, A., Panchanadeswaran, S. and Sours, E. (2023). Addressing Gender Bias to Achieve Ethical AI. [online] IPI Global Observatory. Available at: https://theglobalobservatory.org/2023/03/gender-bias-ethical-artificial-intelligence/ .
Marr, B. (2023). The Evolution Of Data-Driven And AI-Enabled HR. [online] Forbes. Available at: https://www.forbes.com/sites/bernardmarr/2023/12/08/the-evolution-of-data-driven-and-ai-enabled-hr/?sh=5c501fa01549 [Accessed 21 Jan. 2024].
Maurer, R. (2017). Use of AI in the Workplace Raises Legal Concerns. [online] www.shrm.org. Available at: https://www.shrm.org/topics-tools/news/technology/use-ai-workplace-raises-legal-concerns [Accessed 21 Jan. 2024].
Michelman, P. (2020). AI’s Role in Achieving Gender Equity - MIT Sloan Management Review. [online] sloanreview.mit.edu. Available at: https://sloanreview.mit.edu/audio/ais-role-in-achieving-gender-equity/ [Accessed 21 Jan. 2024].
Miller, G.J. (2022). Stakeholder roles in artificial intelligence projects. Project Leadership and Society, [online] 3(100068), p.100068. doi:https://doi.org/10.1016/j.plas.2022.100068.
Nadeem, A., Marjanovic, O. and Abedin, B. (2021). Gender Bias in AI: Implications for Managerial Practices. Responsible AI and Analytics for an Ethical and Inclusive Digitized Society, pp.259–270. doi:https://doi.org/10.1007/978-3-030-85447-8_23.
Neeley, T. (2023). 8 Questions About Using AI Responsibly, Answered. [online] Harvard Business Review. Available at: https://hbr.org/2023/05/8-questions-about-using-ai-responsibly-answered [Accessed 21 Jan. 2024].
O’Connor, S. and Liu, H. (2023). Gender bias perpetuation and mitigation in AI technologies: challenges and opportunities. AI & SOCIETY. doi:https://doi.org/10.1007/s00146-023-01675-4.
Roche, C., Wall, P.J. and Lewis, D. (2022). Ethics and diversity in artificial intelligence policies, strategies and initiatives. AI and Ethics, 3, pp.1095–1115. doi:https://doi.org/10.1007/s43681-022-00218-9.
SHRM (2023). Exploring AI in the Workplace: Regulations, Lawsuits and Best Practices. [online] www.shrm.org. Available at: https://www.shrm.org/topics-tools/news/inclusion-equity-diversity/exploring-ai-in-the-workplace [Accessed 21 Jan. 2024].
Skadden’s 2024 Insights (2023). AI in 2024: Monitoring New Regulation and Staying in Compliance With Existing Laws | Insights | Skadden, Arps, Slate, Meagher & Flom LLP. [online] www.skadden.com. Available at: https://www.skadden.com/insights/publications/2023/12/2024-insights/other-regulatory-developments/ai-in-2024.
Trovato, S. (2023). The Complete Guide to AI Transparency [6 Best Practices]. [online] blog.hubspot.com. Available at: https://blog.hubspot.com/marketing/ai-transparency [Accessed 21 Jan. 2024].
Using artificial intelligence in human resource management. (2023). Human Resource Management International Digest, 32(1), pp.42–44. doi:https://doi.org/10.1108/hrmid-11-2023-0223.
