Keywords
• Artificial Intelligence
• Robotics
• Human Resource Management
• Bias Mitigation
• Data-Driven Decisions
• Ethical Concerns
• Reskilling
Download Article
PDF HTML HTML (Turkish) EPUB
Statistics
• Read: 857 • Download: 740
Submitted: Nov 29, 2024
Accepted: Dec 6, 2024
Published: Dec 24, 2024
Citations
DOI Citation Placeholder Dimensions Badge AI
Table of Contents

This study contributes to the academic discourse by bridging technological advancements with diversity, equity, and inclusion (DEI) practices in HRM. The integration of artificial intelligence (AI) and robotics within human resource management (HRM) is driving a significant transformation in workplace operations. These technologies offer substantial benefits in terms of operational efficiency, decision-making, and innovation, but their application also presents challenges, particularly in the domains of (DEI). AI-powered tools can enhance HR functions by enabling data-driven decision-making and promoting objectivity in recruitment and promotions, thus mitigating biases based on demographic characteristics. However, ethical concerns, including algorithmic biases, and the potential for job displacement due to automation require careful consideration. Organisations must address these challenges through targeted reskilling initiatives, robust monitoring systems, and a strategic commitment to DEI principles. Ensuring that AI and robotics complement human expertise and support an inclusive work environment is crucial for maximising their positive impact. By integrating DEI principles into technological adoption, organisations can create fairer, more equitable workplaces, thus fostering innovation while advancing workplace fairness.

Bayramoğlu, B., & Gülmez, N. (2024). The Role of Big Data, Artificial Intelligence, and Robotics in Human Resource Management: A Diversity, Equity, and Inclusion Perspective. TABAD Journal, 1(1), 1-15. https://tbdjournal.org/ojs/index.php/tbd/article/view/16
Full Article

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, which permits unrestricted non-commercial used, distribution and reproduction in any medium.

Betül Bayramoğlu, Queen Mary University of London

Betül Bayramoğlu, Yıldız Teknik Üniversitesi Endüstri Mühendisliği bölümünden mezun olmuş ve Queen Mary University of London’da International Human Resource Management alanında yüksek lisans yapmıştır. İnsan Kaynakları yönetimi ve yetenek yönetimi konularında uzmanlaşmış olup, bu alanlarda yenilikçi yaklaşımlara odaklanmaktadır.

Dr. Neşe Gülmez, Istanbul Technical University

Neşe Gülmez is PhD and Head of Department of Presidency of the Republic of Türkiye Human Resources Office, Türkiye. Her previous role include Lecturer at Istanbul Technical University, Türkiye. Neşe’s research focuses on career center, talent management and organization, and narcissism.

1. Angwin, J., Larson, J., Mattu, S. and Kirchner, L. (2016). Machine Bias. [online] ProPublica. Available at: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.

2. Azubuike, O.B., Adegboye, O. and Quadri, H. (2020). Who gets to learn in a pandemic? Exploring the digital divide in remote learning during the COVID-19 pandemic in Nigeria. International Journal of Educational Research Open, 2(2), p.100022. doi:https://doi.org/10.1016/j.ijedro.2020.100022.

3. Beretta, E., Vetrò, A., Lepri, B. and Carlos, J. (2021). Detecting discriminatory risk through data annotation based on Bayesian inferences. arXiv (Cornell University), pp.794–804. doi:https://doi.org/10.1145/3442188.3445940.

4. Bibi, M. (2019). Execution of Artificial Intelligence Approach in Human Resource Management Functions: Benefits and Challenges in Pakistan. Sarhad Journal of Management Sciences, 5(1), pp.113–124. doi:https://doi.org/10.31529/sjms.2018.5.1.8.

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

6. Cain, L.N., Thomas, J.H. and Alonso Jr, M. (2019). From sci-fi to sci-fact: the state of robotics and AI in the hospitality industry. Journal of Hospitality and Tourism Technology, ahead-of-print(ahead-of-print). doi:https://doi.org/10.1108/jhtt-07-2018-0066.

7. Carter, C.R., Maki, J., Ackermann, N. and Waters, E.A. (2023). Inclusive Recruitment Strategies to Maximize Sociodemographic Diversity among Participants: A St. Louis Case Study. MDM policy & practice, 8(1). doi:https://doi.org/10.1177/23814683231183646.

8. Chadwick, D., Richards, C., Molin, M. and Strnadová, I. (2023). Digital inclusion and people with learning disabilities. British Journal of Learning Disabilities, 51(2), pp.119–124. doi:https://doi.org/10.1111/bld.12530.

9. Chang, H.H., Hung, C.-J. and Hsieh, H.-W. (2012). Virtual teams: cultural adaptation, communication quality, and interpersonal trust. Total Quality Management & Business Excellence, 25(11-12), pp.1318–1335. doi:https://doi.org/10.1080/14783363.2012.704274.

10. Chang, X. (2023). Gender Bias in Hiring: An Analysis of the Impact of Amazon’s Recruiting Algorithm. Advances in Economics Management and Political Sciences, 23(1), pp.134–140. doi:https://doi.org/10.54254/2754-1169/23/20230367.

11. Chen, Z. (2023). Ethics and Discrimination in Artificial intelligence-enabled Recruitment Practices. Humanities and Social Sciences Communications, [online] 10(1), pp.1–12. doi:https://doi.org/10.1057/s41599-023-02079-x.

12. Chhillar, D. and Aguilera, R.V. (2022). An Eye for Artificial Intelligence: Insights Into the Governance of Artificial Intelligence and Vision for Future Research. Business & Society, 61(5), p.000765032210809. doi:https://doi.org/10.1177/00076503221080959.

13. Cui, X., Xu, B. and Razzaq, A. (2022). Can Application of Artificial Intelligence in Enterprises Promote the Corporate Governance? Frontiers in Environmental Science, 10. doi:https://doi.org/10.3389/fenvs.2022.944467.

14. Dautenhahn, K. (2007). Socially intelligent robots: dimensions of human–robot interaction. Philosophical Transactions of the Royal Society B: Biological Sciences, [online] 362(1480), pp.679–704. doi:https://doi.org/10.1098/rstb.2006.2004.

15. Disability Rights UK (2023). The Future of Remote and Hybrid Working: Listening to the Voices of Disabled Workers by Dr Calum Carson | Disability Rights UK. [online] www.disabilityrightsuk.org. Available at: https://www.disabilityrightsuk.org/news/future-remote-and-hybrid-working-listening-voices-disabled-workers-dr-calum-carson#:~:text=This%20brought%20wider%20benefits%20for [Accessed 31 Mar. 2024].

16. Dixon-Fyle, S., Dolan, K., Hunt, V. and Prince, S. (2020). Diversity wins: How inclusion matters. [online] McKinsey & Company. Available at: https://www.mckinsey.com/featured-insights/diversity-and-inclusion/diversity-wins-how-inclusion-matters.

17. Ekuma, K. (2023). Artificial Intelligence and Automation in Human Resource Development: A Systematic Review. Human Resource Development Review. doi:https://doi.org/10.1177/15344843231224009.

18. Eşkin Bacaksız, F., Yılmaz, M., Ezizi, K. and Alan, H. (2020). Managing Robots in Healthcare. Sağlık ve Hemşirelik Yönetimi Dergisi. doi:https://doi.org/10.5222/shyd.2020.59455.

19. Evseeva, S., Evseeva, O., Burmistrov, A. and Siniavina, M. (2021). Application of artificial intelligence in human resource management in the agricultural sector. E3S Web of Conferences, 258, p.01010. doi:https://doi.org/10.1051/e3sconf/202125801010.

20. Ferrara, E. (2023). Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies. [online] JMIR Preprints. Available at: https://preprints.jmir.org/preprint/48399.

21. Freeman, L., Rahman, A. and Batarseh, F.A. (2021). Enabling Artificial Intelligence Adoption through Assurance. Social Sciences, 10(9), p.322. doi:https://doi.org/10.3390/socsci10090322.

22. GOV.UK (2020). Review into bias in algorithmic decision-making. [online] GOV.UK. Available at: https://www.gov.uk/government/publications/cdei-publishes-review-into-bias-in-algorithmic-decision-making/main-report-cdei-review-into-bias-in-algorithmic-decision-making#recruitment.

23. Guan, X., Feng, X. and Islam, A.Y.M.A. (2023). The dilemma and countermeasures of educational data ethics in the age of intelligence. Humanities and Social Sciences Communications, 10(1). doi:https://doi.org/10.1057/s41599-023-01633-x.

24. Hall, P. and Ellis, D. (2023). A systematic review of socio-technical gender bias in AI algorithms. Online Information Review, 47(7). doi:https://doi.org/10.1108/oir-08-2021-0452.

25. Hays-Thomas, R. (2022). Diversity, Equity, and Inclusion in Organizations. Managing Workplace Diversity, Equity, and Inclusion, pp.3–22. doi:https://doi.org/10.4324/9780367808884-2.

26. Hofeditz, L., Mirbabaie, M., Luther, A., Mauth, R. and Rentemeister, I. (2022). Ethics Guidelines for Using AI-based Algorithms in Recruiting: Learnings from a Systematic Literature Review. [online] scholarspace.manoa.hawaii.edu. Available at: https://scholarspace.manoa.hawaii.edu/items/ad4dcc1a-fa05-40cc-9610-11f3ebde3d40.

27. Hohma, E., Boch, A., Trauth, R. and Lütge, C. (2023). Investigating accountability for Artificial Intelligence through risk governance: A workshop-based exploratory study. Frontiers in Psychology, 14. doi:https://doi.org/10.3389/fpsyg.2023.1073686.

28. 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.

29. Hunkenschroer, A.L. and Luetge, C. (2022). Ethics of AI-Enabled Recruiting and Selection: A Review and Research Agenda. Journal of Business Ethics, [online] 178(4), pp.977–1007. doi:https://doi.org/10.1007/s10551-022-05049-6.

30. Iles, P. and Yolles, M. (2002). Across the great divide: HRD, technology translation, and knowledge migration in bridging the knowledge gap between SMEs and Universities. Human Resource Development International, 5(1), pp.23–53. doi:https://doi.org/10.1080/13678860110007184.

31. Islami, K. and Sopiah, D. (2022). Artificial Intelligence in Human Resources in the Era of Society 5.0. International journal of research and innovation in social science, 06(11), pp.675–681. doi:https://doi.org/10.47772/ijriss.2022.61131.

32. John-Mathews, J.-M., Cardon, D. and Balagué, C. (2022). From Reality to World. A Critical Perspective on AI Fairness. Journal of Business Ethics, 178, pp.945–959. doi:https://doi.org/10.1007/s10551-022-05055-8.

33. Kalmus, M., Hill, H., Goodchild, C., Webb, D. and Lee, J. (2022). A Human Approach to Closing the Digital Divide. [online] BCG Global. Available at: https://www.bcg.com/publications/2022/how-to-close-digital-divide-with-human-approach.

34. Lacroux, A. and Martin-Lacroux, C. (2022). Should I Trust the Artificial Intelligence to Recruit? Recruiters’ Perceptions and Behavior When Faced With Algorithm-Based Recommendation Systems During Resume Screening. Frontiers in Psychology, 13. doi:https://doi.org/10.3389/fpsyg.2022.895997.

35. Li, L. (2022). Reskilling and upskilling the future-ready workforce for industry 4.0 and beyond. Information Systems Frontiers, [online] 24(3), pp.1–16. doi:https://doi.org/10.1007/s10796-022-10308-y.

36. Liu, S., Li, G. and Xia, H. (2021). Analysis of Talent Management in the Artificial Intelligence Era. [online] www.atlantis-press.com. doi:https://doi.org/10.2991/aebmr.k.210218.007.

37. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. and Galstyan, A. (2021). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54(6), pp.1–35. doi:https://doi.org/10.1145/3457607.

38. Mohamed, S.A., Mahmoud, M.A., Mahdi, M.N. and Mostafa, S.A. (2022). Improving Efficiency and Effectiveness of Robotic Process Automation in Human Resource Management. Sustainability, [online] 14(7), p.3920. doi:https://doi.org/10.3390/su14073920.

39. Morandini, S., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D. and Pietrantoni, L. (2023). The Impact of Artificial Intelligence on Workers’ Skills: Upskilling and Reskilling in Organisations. Informing Science: The International Journal of an Emerging Transdiscipline, 26(1), pp.39–68. doi:https://doi.org/10.28945/5078.

40. Nawaz, N. (2019). Artificial Intelligence interchange human intervention in the recruitment process in Indian Software Industry. International Journal of Advanced Trends in Computer Science and Engineering, pp.1433–1441. doi:https://doi.org/10.30534/ijatcse/2019/62842019.

41. Pang, D. and Zhang, Y. (2021). Ethical Principles of Virtual Leadership Construction in Artificial Intelligence Environment. E3S Web of Conferences, 251, p.02023. doi:https://doi.org/10.1051/e3sconf/202125102023.

42. Piatak, J., Dietz, N. and McKeever, B. (2018). Bridging or Deepening the Digital Divide: Influence of Household Internet Access on Formal and Informal Volunteering. Nonprofit and Voluntary Sector Quarterly, 48(2_suppl), pp.123S150S. doi:https://doi.org/10.1177/0899764018794907.

43. Rawal, A., McCoy, J.N., Rawat, D.B., Sadler, B.M. and Robert St. Amant (2021). Recent Advances in Trustworthy Explainable Artificial Intelligence: Status, Challenges and Perspectives. INDIGO (University of Illinois at Chicago), 3(6), pp.852–866. doi:https://doi.org/10.36227/techrxiv.17054396.v1.

44. Renier, L.A., Schmid Mast, M., Dael, N. and Kleinlogel, E.P. (2021). Nonverbal Social Sensing: What Social Sensing Can and Cannot Do for the Study of Nonverbal Behavior From Video. Frontiers in Psychology, 12. doi:https://doi.org/10.3389/fpsyg.2021.606548.

45. Sarti, D. and Torre, T. (2019). Big Data and People Management. Advances in knowledge acquisition, transfer and management book series, pp.127–153. doi:https://doi.org/10.4018/978-1-5225-7077-6.ch006.

46. Shore, L.M., Cleveland, J.N. and Sanchez, D. (2018). Inclusive workplaces: a Review and Model. Human Resource Management Review, 28(2), pp.176–189. doi:https://doi.org/10.1016/j.hrmr.2017.07.003.

47. Silberg, J. and Manyika, J. (2019). Tackling bias in artificial intelligence (and in humans). [online] McKinsey & Company. Available at: https://www.mckinsey.com/featured-insights/artificial-intelligence/tackling-bias-in-artificial-intelligence-and-in-humans.

48. Tambe, P., Cappelli, P. and Yakubovich, V. (2019). Artificial Intelligence in Human Resources Management: Challenges and a Path Forward. California Management Review, 61(4), pp.15–42. doi:https://doi.org/10.1177/0008125619867910.

49. Thompson, S. (2018). Mobile Technology and Inclusion of Persons with Disabilities. [online] Available at: https://assets.publishing.service.gov.uk/media/5b43205a40f0b678b369e262/Mobile_tech_and_inclusion_of_persons_with_disability.pdf.

50. Tóth, Z., Caruana, R., Gruber, T. and Loebbecke, C. (2022). The Dawn of the AI Robots: Towards a New Framework of AI Robot Accountability. Journal of Business Ethics, 178(4), pp.895–916. doi:https://doi.org/10.1007/s10551-022-05050-z.

51. Vivek, R. (2023). Enhancing diversity and reducing bias in recruitment through AI: a review of strategies and challenges. Информатика. Экономика. Управление - Informatics. Economics. Management, [online] 2(4), pp.0101–0118. doi:https://doi.org/10.47813/2782-5280-2023-2-4-0101-0118.

52. Wael, H.A., Abdallah, W., Ghura, H. and Buallay, A. (2023). Factors influencing artificial intelligence adoption in the accounting profession: the case of public sector in Kuwait. Competitiveness review, 34(1). doi:https://doi.org/10.1108/cr-09-2022-0137.

53. Warschauer, M. (2003). Technology and social inclusion : rethinking the digital divide. Cambridge, Mass. ; London: Mit, pp.17–33.

54. Xu, S., Stienmetz, J. and Ashton, M. (2020). How will service robots redefine leadership in hotel management? A Delphi approach. International Journal of Contemporary Hospitality Management, ahead-of-print(ahead-of-print). doi:https://doi.org/10.1108/ijchm-05-2019-0505.

55. Zhang, Y. (2022). Analysis of the Impact of Technological Advances Such as Big Data and AI on Gender Equity of People Management in Organisations. BCP Business & Management, 17, pp.169–175. doi:https://doi.org/10.54691/bcpbm.v17i.389.

56. Zhou, N., Zhang, Z., Nair, V.N., Singhal, H. and Chen, J. (2022). Bias, Fairness and Accountability with Artificial Intelligence and Machine Learning Algorithms. International Statistical Review. [online] doi:https://doi.org/10.1111/insr.12492.