THE DARK SIDE OF AI-ENABLED HRM: PERCEIVED ORGANIZATIONAL INJUSTICE, EMPLOYEE PRIVACY CONCERN, AND WELL-BEING

Hima Barima; Lindawati Sipahutar

doi:10.19166/derema.v21i2.11582

Penulis

Hima Barima Universitas Perwira Purbalingga
Lindawati Sipahutar Universitas Perwira Purbalingga

DOI:

https://doi.org/10.19166/derema.v21i2.11582

Kata Kunci:

AI-enabled HRM, organizational justice, employee privacy, organizational trust, employee well-being

Abstrak

This article develops an employee-centered framework to explain the dark side of AI-enabled human resource management (AI-HRM). Method: Using a structured integrative conceptual review, it synthesizes 52 peer-reviewed articles published between 2019 and 2025, seven foundational sources, and one official report. Findings: Five mechanisms—algorithmic bias, opacity, workplace surveillance, privacy-invasive people analytics, and algorithmic control—influence perceived organizational injustice, employee privacy concern, and work autonomy. These experiences affect psychological, work-related, and relational well-being both directly and indirectly through organizational trust. Four pathway-specific governance capabilities—algorithmic accountability, actionable explainability, privacy-preserving data governance, and meaningful human oversight—can mitigate these risks. The framework is conceptual rather than empirically tested; its ten core propositions therefore require future validation. Contribution: By integrating previously fragmented research on AI-HRM, fairness, privacy, algorithmic management, and well-being, the article offers a parsimonious and testable framework with practical guidance for responsible AI-HRM.

Abstrak dalam Bahasa Indonesia : Artikel ini mengembangkan kerangka yang berpusat pada karyawan untuk menjelaskan sisi gelap manajemen sumber daya manusia berbasis kecerdasan buatan (AI-HRM). Metode: Dengan menggunakan tinjauan konseptual integratif yang terstruktur, artikel ini menyintesis 52 artikel ilmiah yang terbit pada 2019–2025, tujuh rujukan teoretis fundamental, dan satu laporan resmi. Hasil: Lima mekanisme—bias algoritmik, opasitas, pengawasan di tempat kerja, analitik tenaga kerja yang invasif terhadap privasi, dan kontrol algoritmik—memengaruhi persepsi ketidakadilan organisasi, kekhawatiran privasi karyawan, dan otonomi kerja. Pengalaman tersebut memengaruhi kesejahteraan psikologis, pekerjaan, dan relasional, baik secara langsung maupun tidak langsung melalui kepercayaan organisasi. Empat kapabilitas tata kelola yang spesifik pada jalur—akuntabilitas algoritmik, eksplainabilitas yang dapat ditindaklanjuti, tata kelola data yang menjaga privasi, dan pengawasan manusia yang bermakna—dapat mengurangi risiko tersebut. Kerangka ini bersifat konseptual dan belum diuji secara empiris; karena itu, sepuluh proposisi intinya memerlukan validasi pada penelitian mendatang. Kontribusi: Artikel ini mengintegrasikan riset AI-HRM, keadilan, privasi, manajemen algoritmik, dan kesejahteraan ke dalam kerangka yang ringkas, dapat diuji, dan relevan bagi tata kelola AI-HRM yang bertanggung jawab.

Referensi

Andriani, P., Widia, E., & Patiro, S. P. S. (2023). Determination of procedural justice, interactional justice, distributive justice with work motivation as an intervening variable on employee job satisfaction at Raja Ahmad Tabib Hospital. DeReMa (Development Research of Management): Jurnal Manajemen, 18(1), 62–81. https://doi.org/10.19166/derema.v18i1.6574

Bankins, S. (2021). The ethical use of artificial intelligence in human resource management: A decision-making framework. Ethics and Information Technology, 23, 841–854. https://doi.org/10.1007/s10676-021-09619-6

Bankins, S., Formosa, P., Griep, Y., & Richards, D. (2022). AI decision making with dignity? Contrasting workers’ justice perceptions of human and AI decision making in a human resource management context. Information Systems Frontiers, 24(3), 857–875. https://doi.org/10.1007/s10796-021-10223-8

Basu, S., Majumdar, B., Mukherjee, K., Munjal, S., & Palaksha, C. (2023). 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

Black, J. S., & van Esch, P. (2020). AI-enabled recruiting: What is it and how should a manager use it? Business Horizons, 63(2), 215–226. https://doi.org/10.1016/j.bushor.2019.12.001

Budhwar, P., Malik, A., De Silva, M. T. T., & Thevisuthan, P. (2022). Artificial intelligence—Challenges and opportunities for international HRM: A review and research agenda. The International Journal of Human Resource Management, 33(6), 1065–1097. https://doi.org/10.1080/09585192.2022.2035161

Bujold, A., Roberge-Maltais, I., Parent-Rocheleau, X., Boasen, J., Sénécal, S., & Léger, P.-M. (2024). 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

Chowdhury, S., Dey, P. K., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1), 100899. https://doi.org/10.1016/j.hrmr.2022.100899

Colquitt, J. A. (2001). On the dimensionality of organizational justice: A construct validation of a measure. Journal of Applied Psychology, 86(3), 386–400. https://doi.org/10.1037/0021-9010.86.3.386

Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An interdisciplinary review. Journal of Management, 31(6), 874–900. https://doi.org/10.1177/0149206305279602

Culnan, M. J., & Armstrong, P. K. (1999). Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation. Organization Science, 10(1), 104–115. https://doi.org/10.1287/orsc.10.1.104

Curchod, C., Patriotta, G., Cohen, L., & Neysen, N. (2020). Working for an algorithm: Power asymmetries and agency in online work settings. Administrative Science Quarterly, 65(3), 644–676. https://doi.org/10.1177/0001839219867024

Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86(3), 499–512. https://doi.org/10.1037/0021-9010.86.3.499

Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2020). Algorithmic management and app-work in the gig economy: A research agenda for employment relations and HRM. Human Resource Management Journal, 30(1), 114–132. https://doi.org/10.1111/1748-8583.12258

Einola, K., & Khoreva, V. (2023). Best friend or broken tool? Exploring the co-existence of humans and artificial intelligence in the workplace ecosystem. Human Resource Management, 62(1), 117–135. https://doi.org/10.1002/hrm.22147

Folger, N., Brosi, P., Stumpf-Wollersheim, J., & Welpe, I. M. (2022). Applicant reactions to digital selection methods: A signaling perspective on innovativeness and procedural justice. Journal of Business and Psychology, 37(4), 735–757. https://doi.org/10.1007/s10869-021-09770-3

Gagné, M., Parent-Rocheleau, X., Bujold, A., Gaudet, M.-C., & Lirio, P. (2022). How algorithmic management influences worker motivation: A self-determination theory perspective. Canadian Psychology/Psychologie Canadienne, 63(2), 247–260. https://doi.org/10.1037/cap0000324

Giermindl, L. M., Strich, F., Christ, O., Leicht-Deobald, U., & Redzepi, A. (2022). 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

Glavin, P., Bierman, A., & Schieman, S. (2024). Private eyes, they see your every move: Workplace surveillance and worker well-being. Social Currents, 11(4), 327–345. https://doi.org/10.1177/23294965241228874

Grant, A. M., Christianson, M. K., & Price, R. H. (2007). Happiness, health, or relationships? Managerial practices and employee well-being tradeoffs. Academy of Management Perspectives, 21(3), 51–63. https://doi.org/10.5465/amp.2007.26421238

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

Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). 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

Kinowska, H., & Sienkiewicz, L. J. (2023). Influence of algorithmic management practices on workplace well-being: Evidence from European organisations. Information Technology & People, 36(8), 21–42. https://doi.org/10.1108/ITP-02-2022-0079

Köchling, A., & Wehner, M. C. (2020). Discriminated by an algorithm: A systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Business Research, 13, 795–848. https://doi.org/10.1007/s40685-020-00134-w

Köchling, A., Wehner, M. C., & Rühle, S. A. (2025). This (AI)n’t fair? Employee reactions to artificial intelligence (AI) in career development systems. Review of Managerial Science, 19(4), 1195–1228. https://doi.org/10.1007/s11846-024-00789-3

Langer, M., & König, C. J. (2023). Introducing a multi-stakeholder perspective on opacity, transparency and strategies to reduce opacity in algorithm-based human resource management. Human Resource Management Review, 33(1), 100881. https://doi.org/10.1016/j.hrmr.2021.100881

Langer, M., König, C. J., & Hemsing, V. (2020). Is anybody listening? The impact of automatically evaluated job interviews on impression management and applicant reactions. Journal of Managerial Psychology, 35(4), 271–284. https://doi.org/10.1108/JMP-03-2019-0156

Langer, M., König, C. J., & Papathanasiou, M. (2019). Highly automated job interviews: Acceptance under the influence of stakes. International Journal of Selection and Assessment, 27(3), 217–234. https://doi.org/10.1111/ijsa.12246

Lavanchy, M., Reichert, P., Narayanan, J., & Savani, K. (2023). Applicants’ fairness perceptions of algorithm-driven hiring procedures. Journal of Business Ethics, 188(1), 125–150. https://doi.org/10.1007/s10551-022-05320-w

Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019). The challenges of algorithm-based HR decision-making for personal integrity. Journal of Business Ethics, 160(2), 377–392. https://doi.org/10.1007/s10551-019-04204-w

Ly-Le, T.-M., & Le, H. C. P. (2024). The impact of internal communication on Vietnamese Gen Z employees’ work satisfaction. DeReMa (Development Research of Management): Jurnal Manajemen, 19(2), 113–130. https://doi.org/10.19166/derema.v19i2.8165

Malik, A., Budhwar, P., & Kazmi, B. A. (2023). Artificial intelligence (AI)-assisted HRM: Towards an extended strategic framework. Human Resource Management Review, 33(1), 100940. https://doi.org/10.1016/j.hrmr.2022.100940

Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734. https://doi.org/10.5465/amr.1995.9508080335

Meijerink, J., & Bondarouk, T. (2023). The duality of algorithmic management: Toward a research agenda on HRM algorithms, autonomy and value creation. Human Resource Management Review, 33(1), 100876. https://doi.org/10.1016/j.hrmr.2021.100876

Meijerink, J., Boons, M., Keegan, A., & Marler, J. (2021). Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM. The International Journal of Human Resource Management, 32(12), 2545–2562. https://doi.org/10.1080/09585192.2021.1925326

Mori, M., Sassetti, S., Cavaliere, V., & Bonti, M. (2025). A systematic literature review on artificial intelligence in recruiting and selection: A matter of ethics. Personnel Review, 54(3), 854–878. https://doi.org/10.1108/PR-03-2023-0257

Newman, D. T., Fast, N. J., & Harmon, D. J. (2020). When eliminating bias isn’t fair: Algorithmic reductionism and procedural justice in human resource decisions. Organizational Behavior and Human Decision Processes, 160, 149–167. https://doi.org/10.1016/j.obhdp.2020.03.008

Oostrom, J. K., Holtrop, D., Koutsoumpis, A., van Breda, W., Ghassemi, S., & de Vries, R. E. (2024). Applicant reactions to algorithm- versus recruiter-based evaluations of an asynchronous video interview and a personality inventory. Journal of Occupational and Organizational Psychology, 97(1), 160–189. https://doi.org/10.1111/joop.12465

Ore, O., & Sposato, M. (2022). Opportunities and risks of artificial intelligence in recruitment and selection. International Journal of Organizational Analysis, 30(6), 1771–1782. https://doi.org/10.1108/IJOA-07-2020-2291

Pan, Y., & Froese, F. J. (2023). An interdisciplinary review of AI and HRM: Challenges and future directions. Human Resource Management Review, 33(1), 100924. https://doi.org/10.1016/j.hrmr.2022.100924

Parent-Rocheleau, X., & Parker, S. K. (2022). Algorithms as work designers: How algorithmic management influences the design of jobs. Human Resource Management Review, 32(3), 100838. https://doi.org/10.1016/j.hrmr.2021.100838

Pessach, D., & Shmueli, E. (2022). A review on fairness in machine learning. ACM Computing Surveys, 55(3), 51. https://doi.org/10.1145/3494672

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

Qamar, Y., Agrawal, R. K., Samad, T. A., & Chiappetta Jabbour, C. J. (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

Qin, M. S., Jia, N., Luo, X., Liao, C., & Huang, Z. (2023). Perceived fairness of human managers compared with artificial intelligence in employee performance evaluation. Journal of Management Information Systems, 40(4), 1039–1070. https://doi.org/10.1080/07421222.2023.2267316

Ravid, D. M., Tomczak, D. L., White, J. C., & Behrend, T. S. (2020). EPM 20/20: A review, framework, and research agenda for electronic performance monitoring. Journal of Management, 46(1), 100–126. https://doi.org/10.1177/0149206319869435

Republic of Indonesia. (2022). Undang-Undang Republik Indonesia Nomor 27 Tahun 2022 tentang Pelindungan Data Pribadi. https://peraturan.bpk.go.id/Details/229798/uu-no-27-tahun-2022

Robert, L. P., Pierce, C., Marquis, L., Kim, S., & Alahmad, R. (2020). Designing fair AI for managing employees in organizations: A review, critique, and design agenda. Human-Computer Interaction, 35(5–6), 545–575. https://doi.org/10.1080/07370024.2020.1735391

Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y., & Tarba, S. Y. (2023). An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Human Resource Management Review, 33(1), 100925. https://doi.org/10.1016/j.hrmr.2022.100925

Soleimani, M., Intezari, A., Arrowsmith, J., Pauleen, D. J., & Taskin, N. (2025). Reducing AI bias in recruitment and selection: An integrative grounded approach. The International Journal of Human Resource Management, 36(14), 2480–2515. https://doi.org/10.1080/09585192.2025.2480617

Starke, C., Baleis, J., Keller, B., & Marcinkowski, F. (2022). Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature. Big Data & Society, 9(2), 20539517221115189. https://doi.org/10.1177/20539517221115189

Strohmeier, S. (2020). Digital human resource management: A conceptual clarification. German Journal of Human Resource Management, 34(3), 345–365. https://doi.org/10.1177/2397002220921131

Suen, H.-Y., Chen, M. Y.-C., & Lu, S.-H. (2019). Does the use of synchrony and artificial intelligence in video interviews affect interview ratings and applicant attitudes? Computers in Human Behavior, 98, 93–101. https://doi.org/10.1016/j.chb.2019.04.012

Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910

Tinguely, P. N., Lee, J., & He, V. F. (2023). Designing human resource management systems in the age of AI. Journal of Organization Design, 12(4), 263–269. https://doi.org/10.1007/s41469-023-00153-x

Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting. Human Relations, 4(1), 3–38. https://doi.org/10.1177/001872675100400101

Tursunbayeva, A., Pagliari, C., Di Lauro, S., & Antonelli, G. (2022). The ethics of people analytics: Risks, opportunities and recommendations. Personnel Review, 51(3), 900–921. https://doi.org/10.1108/PR-12-2019-0680

Van Esch, P., & Black, J. S. (2019). Factors that influence new generation candidates to engage with and complete digital, AI-enabled recruiting. Business Horizons, 62(6), 729–739. https://doi.org/10.1016/j.bushor.2019.07.004

Varma, A., Dawkins, C., & Chaudhuri, K. (2023). Artificial intelligence and people management: A critical assessment through the ethical lens. Human Resource Management Review, 33(1), 100923. https://doi.org/10.1016/j.hrmr.2022.100923

Vitak, J., & Zimmer, M. (2023). Surveillance and the future of work: Exploring employees’ attitudes toward monitoring in a post-COVID workplace. Journal of Computer-Mediated Communication, 28(4), zmad007. https://doi.org/10.1093/jcmc/zmad007

Vrontis, D., Christofi, M., Pereira, V., Tarba, S. Y., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. The International Journal of Human Resource Management, 33(6), 1237–1266. https://doi.org/10.1080/09585192.2020.1871398

World Economic Forum. (2025). The future of jobs report 2025. World Economic Forum. https://www.weforum.org/publications/the-future-of-jobs-report-2025/

Zhou, Y., Wang, L., & Chen, W. (2023). The dark side of AI-enabled HRM on employees based on AI algorithmic features. Journal of Organizational Change Management, 36(7), 1222–1241. https://doi.org/10.1108/JOCM-10-2022-0308