Digital Human Resource Management for Inclusive and Sustainable Workplaces A Systematic Review of Diversity, Equity, Inclusion, and Algorithmic Fairness
Main Article Content
Abstract
The integration of artificial intelligence (AI) into human resource management (HRM) creates opportunities for efficiency while raising concerns about algorithmic bias, discrimination, and workplace inequality. This study systematically reviews literature on digital HRM, diversity, equity, and inclusion (DEI), and algorithmic fairness to examine their implications for inclusive and sustainable workplaces. Guided by PRISMA 2020 and informed by PRISMA-S, the review synthesizes conceptual and empirical research on AI-enabled HRM, workplace inclusion, and sustainable human resource practices. The findings indicate that algorithmic systems may reproduce existing inequalities through biased training data, proxy variables, and design assumptions. Although AI can improve recruitment and operational efficiency, fair outcomes require transparency, accountability, human oversight, and continuous evaluation. The review also identifies limited integrated empirical evidence connecting algorithmic fairness, DEI, and sustainable HRM, particularly regarding long-term employee well-being and organizational outcomes. These findings emphasize the importance of embedding fairness and inclusion into digital HRM governance. Future research should prioritize real-world evaluations, intersectional fairness, and longitudinal investigations of employee autonomy and workplace sustainability.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Al‐Alawi, A. I., Messaadia, M., Mehrotra, A. A., Sanosi, S. K., Elias, H., & Althawadi, A. H. (2023). Digital transformation adoption in human resources management during COVID-19. Arab Gulf Journal of Scientific Research, 41(4), 446–461. https://doi.org/10.1108/agjsr-05-2022-0069
Alfrink, K., Keller, I., Kortuem, G., & Doorn, N. (2022). Contestable AI by Design: Towards a Framework. Minds and Machines, 33(4), 613–639. https://doi.org/10.1007/s11023-022-09611-z
Al-Harazneh, Y. M., & Sila, I. (2021). The Impact of E-HRM Usage on HRM Effectiveness. Journal of Global Information Management, 29(2), 118–147. https://doi.org/10.4018/jgim.2021030107
Ali, M., Sapieżyński, P., Bogen, M., Korolova, A., Mislove, A., & Rieke, A. (2019). Discrimination through Optimization. Proceedings of the ACM on Human-Computer Interaction, 3, 1–30. https://doi.org/10.1145/3359301
Allhutter, D., Cech, F., Fischer, F., Grill, G., & Mager, A. (2020). Algorithmic Profiling of Job Seekers in Austria: How Austerity Politics Are Made Effective. Frontiers in Big Data, 3, 5. https://doi.org/10.3389/fdata.2020.00005
Álvarez, J. M., Colmenarejo, A. B., Elobaid, A., Fabbrizzi, S., Fahimi, M., Ferrara, A., Ghodsi, S., Mougan, C., Papageorgiou, I., Lobo, P. R., Russo, M., Scott, K. M., State, L., Zhao, X., & Ruggieri, S. (2024). Policy advice and best practices on bias and fairness in AI. Ethics and Information Technology, 26(2). https://doi.org/10.1007/s10676-024-09746-w
Ayling, J., & Chapman, A. (2021). Putting AI ethics to work: are the tools fit for purpose? AI and Ethics, 2(3), 405–429. https://doi.org/10.1007/s43681-021-00084-x
Basu, S., Majumdar, B., Mukherjee, K., Munjal, S., & Palaksha, C. (2022). Artificial Intelligence–HRM Interactions and Outcomes: A Systematic Review and Causal Configurational Explanation. Human Resource Management Review, 33(1), 100893. https://doi.org/10.1016/j.hrmr.2022.100893
Benlian, A., Wiener, M., Cram, W. A., Krasnova, H., Maedche, A., Möhlmann, M., Recker, J., & Remus, U. (2022). Algorithmic Management. Business & Information Systems Engineering, 64(6), 825–839. https://doi.org/10.1007/s12599-022-00764-w
Boekhorst, J. A. (2014). The Role of Authentic Leadership in Fostering Workplace Inclusion: A Social Information Processing Perspective. Human Resource Management, 54(2), 241–264. https://doi.org/10.1002/hrm.21669
Böhmer, N., & Schinnenburg, H. (2023). Critical exploration of AI-driven HRM to build up organizational capabilities. University of Applied Sciences Library (University of Osnabrueck), 45(5), 1057–1082. https://doi.org/10.1108/er-04-2022-0202
Bondarouk, T., Parry, E., & Furtmueller, E. (2016). Electronic HRM: four decades of research on adoption and consequences. The International Journal of Human Resource Management, 28(1), 98–131. https://doi.org/10.1080/09585192.2016.1245672
Bondarouk, T., & Ruël, H. (2009). Electronic Human Resource Management: challenges in the digital era. University of Twente Research Information, 20(3), 505–514. https://doi.org/10.1080/09585190802707235
Branicki, L., Brammer, S., Brosnan, M., García-Lazaro, A., Lattanzio, S., & Newnes, L. (2024). Factors shaping the employment outcomes of neurodivergent and neurotypical people: Exploring the role of flexible and homeworking practices. Human Resource Management, 63(6), 1001–1023. https://doi.org/10.1002/hrm.22243
Bujold, A., Roberge‐Maltais, I., Parent‐Rocheleau, X., Boasen, J., Sénécal, S., & Léger, P. (2023). Responsible artificial intelligence in human resources management: a review of the empirical literature. AI and Ethics, 4(4), 1185–1200. https://doi.org/10.1007/s43681-023-00325-1
Burrell, J., & Fourcade, M. (2021). The Society of Algorithms. Annual Review of Sociology, 47(1), 213–237. https://doi.org/10.1146/annurev-soc-090820-020800
Carey, A. N., & Wu, X. (2022). The statistical fairness field guide: perspectives from social and formal sciences. AI and Ethics, 3(1), 1–23. https://doi.org/10.1007/s43681-022-00183-3
Carmeli, A., Reiter‐Palmon, R., & Ziv, E. (2010). Inclusive Leadership and Employee Involvement in Creative Tasks in the Workplace: The Mediating Role of Psychological Safety. Creativity Research Journal, 22(3), 250–260. https://doi.org/10.1080/10400419.2010.504654
Carter, D., D’Souza, F. P., Simkins, B. J., & Simpson, W. (2010). The Gender and Ethnic Diversity of US Boards and Board Committees and Firm Financial Performance. Corporate Governance An International Review, 18(5), 396–414. https://doi.org/10.1111/j.1467-8683.2010.00809.x
Chen, Z. (2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-02079-x
Chiu, Y.-T., Zhu, Y., & Corbett, J. (2021). In the hearts and minds of employees: A model of pre-adoptive appraisal toward artificial intelligence in organizations. International Journal of Information Management, 60, 102379. https://doi.org/10.1016/j.ijinfomgt.2021.102379
Chrobot‐Mason, D., Hoobler, J. M., & Burno, J. (2018). Lean InVersus the Literature: An Evidence-Based Examination. Academy of Management Perspectives, 33(1), 110–130. https://doi.org/10.5465/amp.2016.0156
Cole, M., Cant, C., Spilda, F. U., & Graham, M. (2022). Politics by Automatic Means? A Critique of Artificial Intelligence Ethics at Work. Frontiers in Artificial Intelligence, 5, 869114. https://doi.org/10.3389/frai.2022.869114
Cooke, F. L., Dickmann, M., & Parry, E. (2022). Building sustainable societies through human-centred human resource management: emerging issues and research opportunities. The International Journal of Human Resource Management, 33(1), 1–15. https://doi.org/10.1080/09585192.2021.2021732
Daniel, W., Andreas, G., & Kilian, K. (2025). Scaling of End-To-End Governance Risk Assessments for AI Systems (Practitioner Track). In Dagstuhl Research Online Publication Server. https://doi.org/10.4230/oasics.saia.2024.4
Drage, E., & Mackereth, K. (2022). Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference.” Philosophy & Technology, 35(4), 89. https://doi.org/10.1007/s13347-022-00543-1
Ekstrand, M. D., Das, A., Burke, R., & Díaz, F. (2022). Fairness in Information Access Systems. arXiv (Cornell University), 16, 1–177. https://doi.org/10.1561/1500000079
Fabris, A., Baranowska, N., Dennis, M., Graus, D., Hacker, P., Saldivar, J., Borgesius, F. Z., & Biega, A. J. (2024). Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey. UvA-DARE (University of Amsterdam), 16(1), 1–54. https://doi.org/10.1145/3696457
Félix, B., Dourado, D., & Nossa, V. (2023). Algorithmic management, preferences for autonomy/security and gig-workers’ wellbeing: A matter of fit? Frontiers in Psychology, 14, 1088183. https://doi.org/10.3389/fpsyg.2023.1088183
Fenwick, A., Molnár, G., & Frangos, P. (2024). Revisiting the role of HR in the age of AI: bringing humans and machines closer together in the workplace. Frontiers in Artificial Intelligence, 6, 1272823. https://doi.org/10.3389/frai.2023.1272823
Ferrario, A., & Loi, M. (2022). The Robustness of Counterfactual Explanations Over Time. IEEE Access, 10, 82736–82750. https://doi.org/10.1109/access.2022.3196917
Foster, B. P., Manikas, A., & Kroes, J. R. (2022). Which diversity measures best capture public company value? Corporate Social Responsibility and Environmental Management, 30(1), 236–247. https://doi.org/10.1002/csr.2351
Giermindl, L., Strich, F., Christ, O., Leicht‐Deobald, U., & Redzepi, A. (2021). The dark sides of people analytics: reviewing the perils for organisations and employees. European Journal of Information Systems, 31(3), 410–435. https://doi.org/10.1080/0960085x.2021.1927213
Granulo, A., Caprioli, S., Fuchs, C., & Puntoni, S. (2023). Deployment of algorithms in management tasks reduces prosocial motivation. Computers in Human Behavior, 152, 108094. https://doi.org/10.1016/j.chb.2023.108094
Graziani, M., Dutkiewicz, L., Calvaresi, D., Amorim, J. P., Yordanova, K., Vered, M., Nair, R., Abreu, P. H., Blanke, T., Pulignano, V., Prior, J. O., Lauwaert, L., Reijers, W., Depeursinge, A., Andrearczyk, V., & Müller, H. (2022). A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences. Artificial Intelligence Review, 56(4), 3473–3504. https://doi.org/10.1007/s10462-022-10256-8
Guidotti, R. (2022). Counterfactual explanations and how to find them: literature review and benchmarking. Data Mining and Knowledge Discovery, 38(5), 2770–2824. https://doi.org/10.1007/s10618-022-00831-6
Guillaume, Y., Dawson, J., Priola, V., Sacramento, C. A., Woods, S. A., Higson, H., Budhwar, P., & West, M. (2013). Managing diversity in organizations: An integrative model and agenda for future research. European Journal of Work and Organizational Psychology, 23(5), 783–802. https://doi.org/10.1080/1359432x.2013.805485
Gyensare, M. A., Adomako, S., & Amankwah‐Amoah, J. (2023). Green HRM practices, employee well‐being, and sustainable work behavior: Examining the moderating role of resource commitment. Business Strategy and the Environment, 33(4), 3129–3141. https://doi.org/10.1002/bse.3642
Henckaerts, R., Antonio, K., & Côté, M. (2022). When stakes are high: Balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates. Expert Systems with Applications, 202, 117230. https://doi.org/10.1016/j.eswa.2022.117230
Hofeditz, L., Clausen, S., Rieß, A., Mirbabaie, M., & Stieglitz, S. (2022). Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring. Electronic Markets, 32(4), 2207–2233. https://doi.org/10.1007/s12525-022-00600-9
Holmes, O., Jiang, K., Avery, D. R., McKay, P. F., Oh, I., & Tillman, C. J. (2020). A Meta-Analysis Integrating 25 Years of Diversity Climate Research. Journal of Management, 47(6), 1357–1382. https://doi.org/10.1177/0149206320934547
Hunkenschroer, A. L., & Luetge, C. (2022). Ethics of AI-Enabled Recruiting and Selection: A Review and Research Agenda. Journal of Business Ethics, 178(4), 977–1007. https://doi.org/10.1007/s10551-022-05049-6
Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021). Algorithmic management in a work context. Big Data & Society, 8(2). https://doi.org/10.1177/20539517211020332
Kassir, S., Baker, L., Dolphin, J., & Polli, F. (2022). AI for hiring in context: a perspective on overcoming the unique challenges of employment research to mitigate disparate impact. AI and Ethics, 3(3), 845–868. https://doi.org/10.1007/s43681-022-00208-x
Kellogg, K. C., Valentine, M., & Christin, A. (2019). Algorithms at Work: The New Contested Terrain of Control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174
Kim, S., Khoreva, V., & Vaiman, V. (2024). Strategic Human Resource Management in the Era of Algorithmic Technologies: Key Insights and Future Research Agenda. Human Resource Management, 64(2), 447–464. https://doi.org/10.1002/hrm.22268
Kinowska, H., & Sienkiewicz, Ł. (2022). Influence of algorithmic management practices on workplace well-being – evidence from European organisations. Information Technology and People, 36(8), 21–42. https://doi.org/10.1108/itp-02-2022-0079
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017). Inherent Trade-Offs in the Fair Determination of Risk Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. arXiv (Cornell University), 1(11), 501–507. https://doi.org/10.1038/s42256-019-0114-4
Mökander, J. (2023). Auditing of AI: Legal, Ethical and Technical Approaches. Digital Society, 2(3). https://doi.org/10.1007/s44206-023-00074-y
Mökander, J., & Floridi, L. (2021). Ethics-Based Auditing to Develop Trustworthy AI. Minds and Machines, 31(2), 323–327. https://doi.org/10.1007/s11023-021-09557-8
Mökander, J., Morley, J., Taddeo, M., & Floridi, L. (2021). Ethics-Based Auditing of Automated Decision-Making Systems: Nature, Scope, and Limitations. Science and Engineering Ethics, 27(4), 44. https://doi.org/10.1007/s11948-021-00319-4
Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2019). From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices. Science and Engineering Ethics, 26(4), 2141–2168. https://doi.org/10.1007/s11948-019-00165-5
Mulligan, D. K., Kroll, J. A., Kohli, N., & Wong, R. Y. (2019). This Thing Called Fairness. Proceedings of the ACM on Human-Computer Interaction, 3, 1–36. https://doi.org/10.1145/3359221
Munn, L. (2022). The uselessness of AI ethics. AI and Ethics, 3(3), 869–877. https://doi.org/10.1007/s43681-022-00209-w
Nittrouer, C., Arena, D. F., Silver, E. R., Avery, D. R., & Hebl, M. (2024). Despite the haters: The immense promise and progress of diversity, equity, and inclusion initiatives. Journal of Organizational Behavior, 46(1), 188–201. https://doi.org/10.1002/job.2835
Noponen, N., Feshchenko, P., Auvinen, T., Luoma‐aho, V., & Abrahamsson, P. (2023). Taylorism on steroids or enabling autonomy? A systematic review of algorithmic management. Management Review Quarterly, 74(3), 1695–1721. https://doi.org/10.1007/s11301-023-00345-5
Nyathi, M., & Kekwaletswe, R. M. (2023). Electronic human resource management (e-HRM) configuration for organizational success: inclusion of employee outcomes as contextual variables. Journal of Organizational Effectiveness People and Performance, 11(1), 196–212. https://doi.org/10.1108/joepp-08-2022-0237
Okatta, C. G., Ajayi, F. A., & Olawale, O. (2024). ENHANCING ORGANIZATIONAL PERFORMANCE THROUGH DIVERSITY AND INCLUSION INITIATIVES: A META-ANALYSIS. International Journal of Applied Research in Social Sciences, 6(4), 734–758. https://doi.org/10.51594/ijarss.v6i4.1065
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T., Mulrow, C. D., Shamseer, L., Tetzlaff, J., Akl, E. A., Brennan, S., Chou, R., Glanville, J., Grimshaw, J., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E., Mayo‐Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Systematic Reviews, 10(1), 89. https://doi.org/10.1186/s13643-021-01626-4
Palos‐Sánchez, P. R., Baena‐Luna, P., Badicu, A., & Infante-Moro, J. C. (2022). Artificial Intelligence and Human Resources Management: A Bibliometric Analysis. Applied Artificial Intelligence, 36(1). https://doi.org/10.1080/08839514.2022.2145631
Parry, E. (2011). An examination of e-HRM as a means to increase the value of the HR function. CERES (Cranfield University), 22(5), 1146–1162. https://doi.org/10.1080/09585192.2011.556791
Parry, E., & Tyson, S. (2010). Desired goals and actual outcomes of e-HRM. Human Resource Management Journal, 21(3), 335–354. https://doi.org/10.1111/j.1748-8583.2010.00149.x
Pastor-Escuredo, D., Treleaven, P., & Vinuesa, R. (2022). An Ethical Framework for Artificial Intelligence and Sustainable Cities. AI, 3(4), 961–974. https://doi.org/10.3390/ai3040057
Prikshat, V., Islam, M., Patel, P., Malik, A., Budhwar, P., & Gupta, S. (2023). AI-Augmented HRM: Literature review and a proposed multilevel framework for future research. Technological Forecasting and Social Change, 193, 122645. https://doi.org/10.1016/j.techfore.2023.122645
Prikshat, V., Malik, A., & Budhwar, P. (2021). AI-augmented HRM: Antecedents, assimilation and multilevel consequences. Human Resource Management Review, 33(1), 100860. https://doi.org/10.1016/j.hrmr.2021.100860
Priyashantha, K. G. (2022). Disruptive technologies for human resource management: a conceptual framework development and research agenda. Journal of Work-Applied Management, 15(1), 21–36. https://doi.org/10.1108/jwam-10-2022-0069
Priyashantha, K. G., Alwis, D., & Welmilla, I. (2022). Disruptive human resource management technologies: a systematic literature review. European Journal of Management and Business Economics, 33(1), 116–136. https://doi.org/10.1108/ejmbe-01-2022-0018
Qamar, Y., Agrawal, R., Samad, T. A., & Jabbour, C. J. C. (2021). When technology meets people: the interplay of artificial intelligence and human resource management. Journal of Enterprise Information Management, 34(5), 1339–1370. https://doi.org/10.1108/jeim-11-2020-0436
Rethlefsen, M. L., Kirtley, S., Waffenschmidt, S., Ayala, A. P., Moher, D., Page, M. J., Koffel, J., Group, P.-S., Blunt, H. B., Brigham, T., Chang, S., Clark, J., Conway, A., Couban, R., Kock, S. de, Farrah, K., Fehrmann, P., Foster, M., Fowler, S. A., … Young, S. (2021). PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews. Systematic Reviews, 10(1), 39. https://doi.org/10.1186/s13643-020-01542-z
Rhea, A. K., Markey, K., D’Arinzo, L., Schellmann, H., Sloane, M., Squires, P. C., Khan, F. A., & Stoyanovich, J. (2022). An external stability audit framework to test the validity of personality prediction in AI hiring. Data Mining and Knowledge Discovery, 36(6), 2153–2193. https://doi.org/10.1007/s10618-022-00861-0
Richards, J. (2020). Putting employees at the centre of sustainable HRM: a review, map and research agenda. Employee Relations, 44(3), 533–554. https://doi.org/10.1108/er-01-2019-0037
Rigotti, C., & Fosch‐Villaronga, E. (2024). Fairness, AI & recruitment. Leiden Repository (Leiden University), 53, 105966. https://doi.org/10.1016/j.clsr.2024.105966
Roche, C., Wall, P. J., & Lewis, D. (2022). Ethics and diversity in artificial intelligence policies, strategies and initiatives. AI and Ethics, 3(4), 1095–1115. https://doi.org/10.1007/s43681-022-00218-9
Rollnik‐Sadowska, E., & Grabińska, V. (2024). Managing Neurodiversity in Workplaces: A Review and Future Research Agenda for Sustainable Human Resource Management. Sustainability, 16(15), 6594. https://doi.org/10.3390/su16156594
Sannon, S., & Cosley, D. (2022). Toward a More Inclusive Gig Economy: Risks and Opportunities for Workers with Disabilities. Proceedings of the ACM on Human-Computer Interaction, 6, 1–31. https://doi.org/10.1145/3555755
Schiff, D., Kelley, S., & Ibáñez, J. C. (2024). The emergence of artificial intelligence ethics auditing. Big Data & Society, 11(4). https://doi.org/10.1177/20539517241299732
Shore, L. M., Randel, A. E., Chung, B. G., Dean, M. A., Ehrhart, K. H., & Singh, G. (2010). Inclusion and Diversity in Work Groups: A Review and Model for Future Research. Journal of Management, 37(4), 1262–1289. https://doi.org/10.1177/0149206310385943
Simon, J., Wong, P., & Rieder, G. (2020). Algorithmic bias and the Value Sensitive Design approach. Internet Policy Review, 9(4). https://doi.org/10.14763/2020.4.1534
Singh, A., & Pandey, J. (2024). Artificial intelligence adoption in extended HR ecosystems: enablers and barriers. An abductive case research. Frontiers in Psychology, 14, 1339782. https://doi.org/10.3389/fpsyg.2023.1339782
Sloane, M., Moss, E., & Chowdhury, R. (2022). A Silicon Valley love triangle: Hiring algorithms, pseudo-science, and the quest for auditability. Patterns, 3(2), 100425. https://doi.org/10.1016/j.patter.2021.100425
Smith, A. N., Watkins, M. B., Ladge, J., & Carlton, P. (2019). Making the Invisible Visible: Paradoxical Effects of Intersectional Invisibility on the Career Experiences of Executive Black Women. Academy of Management Journal, 62(6), 1705–1734. https://doi.org/10.5465/amj.2017.1513
Spiekermann, S., Krasnova, H., Hinz, O., Baumann, A., Benlian, A., Gimpel, H., Heimbach, I., Köster, A., Maedche, A., Niehaves, B., Risius, M., & Trenz, M. (2022). Values and Ethics in Information Systems. Business & Information Systems Engineering, 64(2), 247–264. https://doi.org/10.1007/s12599-021-00734-8
Stahl, G. K., Brewster, C., Collings, D. G., & Hajro, A. (2019). Enhancing the role of human resource management in corporate sustainability and social responsibility: A multi-stakeholder, multidimensional approach to HRM. Human Resource Management Review, 30(3), 100708. https://doi.org/10.1016/j.hrmr.2019.100708
Stamarski, C. S., & Hing, L. S. S. (2015). Gender inequalities in the workplace: the effects of organizational structures, processes, practices, and decision makers’ sexism. Frontiers in Psychology, 6, 1400. https://doi.org/10.3389/fpsyg.2015.01400
Strohmeier, S. (2020). Digital human resource management: A conceptual clarification. German Journal of Human Resource Management Zeitschrift Für Personalforschung, 34(3), 345–365. https://doi.org/10.1177/2397002220921131
Thatcher, S. M. B., Hymer, C., & Arwine, R. P. (2023). Pushing Back Against Power: Using a Multilevel Power Lens to Understand Intersectionality in the Workplace. Academy of Management Annals, 17(2), 710–750. https://doi.org/10.5465/annals.2021.0210
Tursunbayeva, A., Pagliari, C., Lauro, S. D., & Antonelli, G. (2021). The ethics of people analytics: risks, opportunities and recommendations. Personnel Review, 51(3), 900–921. https://doi.org/10.1108/pr-12-2019-0680
Vassilopoulou, J., Kyriakidou, O., Özbilgin, M. F., & Groutsis, D. (2022). Scientism as illusio in HR algorithms: Towards a framework for algorithmic hygiene for bias proofing. Human Resource Management Journal, 34(2), 311–325. https://doi.org/10.1111/1748-8583.12430
Verma, S., Boonsanong, V., Hoang, M., Hines, K., Dickerson, J. P., & Shah, C. (2024). Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review. ACM Computing Surveys, 56(12), 1–42. https://doi.org/10.1145/3677119
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Nerini, F. F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), 233. https://doi.org/10.1038/s41467-019-14108-y
Votto, A. M., Valecha, R., Najafirad, P., & Rao, H. R. (2021). Artificial Intelligence in Tactical Human Resource Management: A Systematic Literature Review. International Journal of Information Management Data Insights, 1(2), 100047. https://doi.org/10.1016/j.jjimei.2021.100047
Wallrich, L., Opara, V., Wesołowska, M., Barnoth, D., & Yousefi, S. (2024). The Relationship Between Team Diversity and Team Performance: Reconciling Promise and Reality Through a Comprehensive Meta-Analysis Registered Report. ResearchSPAce (Bath Spa University), 39(6), 1303–1354. https://doi.org/10.1007/s10869-024-09977-0
Yam, J., & Skorburg, J. A. (2021). From human resources to human rights: Impact assessments for hiring algorithms. Ethics and Information Technology, 23(4), 611–623. https://doi.org/10.1007/s10676-021-09599-7
Yang, K., Loftus, J. R., & Stoyanovich, J. (2021). Causal Intersectionality and Fair Ranking. In DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). Schloss Dagstuhl – Leibniz Center for Informatics. https://doi.org/10.4230/lipics.forc.2021.7
Yarger, L., Payton, F. C., & Neupane, B. (2019). Algorithmic equity in the hiring of underrepresented IT job candidates. Online Information Review, 44(2), 383–395. https://doi.org/10.1108/oir-10-2018-0334