The Role of Artificial Intelligence in Staffing: An Evaluation of Fairness and Ethical Implications for Candidate Selection
DOI:
https://doi.org/10.47604/jhrl.3786Keywords:
Artificial Intelligence, Recruitment, Selection, Staffing, Efficiency, Recruitment Bias, Ethical RecruitmentAbstract
Purpose: AI utilization has seen an increased adoption in the recruitment processes of organizations, hailed mainly for its ability to reduce time and effort in manual and repetitive tasks.
Methodology: The recruitment process witnessed significant changes, with further automation in resume collection, screening and even candidate shortlisting. An online survey was employed with 160 HR professionals from diverse industry sectors and a quantitative, deductive approach was taken, analyzing data with SPSS (descriptive statistics, correlation, regression). While AI is gaining popularity, the research reveals the risks associated with reliance on algorithms, primarily in the form of bias. Whether stemmed from the coding process, or in the process of machine learning that amplifies a slight bias, the study illustrates the various touch points to particularly consider in mitigating the risks of AI-driven bias in recruitment.
Results: AI-based staffing has a positive effect on unbiased and ethical selection (β = 0.957, p < 0.001) and staffing efficiency (β = 0.816, p < 0.001). But, the relationship between AI-based staffing and staffing efficiency was not mediated by unbiased and ethical selection (β = 0.002, p = 0.992). Existing studies on AI, recruitment, and biases provide the necessary background in drawing links between bias and the efficiencies of AI in the recruitment processes. The study hypothesizes that AI provides unprecedented efficiencies in standardizing processes, optimizing time and allowing recruiters to focus on more strategic decision-making. However, the study also reveals the risks of AI bias, whether from the coding stages of the algorithm or the clinically objective nature of the screening process that could unintentionally eliminate valid and eligible candidates.
Unique Contribution to Theory, Practice and Policy: Theoretically, it explains how the link between AI and efficiency does not go through ethical selection. As a practical solution, it suggests using the combination of human-AI model. It calls for policy recommendations of ethical guidelines and audits.
Downloads
References
Abdelraouf, M. (2024). The impact of artificial intelligence (A.I) on recruitment and selection of human resources management. King, 15(1), 424-454.
Abdelraouf, M. (2024). Artificial intelligence applications in modern human resource management: Balancing efficiency and ethics. Nile University Press.
Almeida, F., Silva, A. J., Lopes, S. L., & Braz, I. (2025). Understanding recruiters' acceptance of artificial intelligence: Insights from the Technology Acceptance Model. Applied Sciences, 15(2), 746.
Ami, R. (2021). AI in automated content moderation on social media. International Journal of Artificial Intelligence and Machine Learning, 4(3).
Buiten, M. C., De Streel, A., & Peitz, M. (2020). Rethinking liability rules for online hosting platforms. International Journal of Law and Information Technology, 28(2), 139-166.
Carman IV, E. F. (2024). Controlling the narrative: How social media companies exploit Section 230 immunity to censor online speech. Mississippi Law Journal, 94, 599.
Charkra, S., Dey, S., John, A., & Kanti, S. (2024). The human factor in AI decision-making: Mitigating bias and error. Research Gate.
Chen, L. (2023). Algorithmic fairness in recruitment: Assessing bias in AI-powered hiring systems. Journal of Business Ethics and Technology, 18(3), 201-219.
Chen, Z. (2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanities and Social Sciences Communications, 10.
Cheong, B. C. (2024). Transparency and accountability in AI systems: Safeguarding wellbeing in the age of algorithmic decision-making. Frontiers in Human Dynamics, 6, 1421273.
Draude, C., Klumbyte, G., Lücking, P., & Treusch, P. (2020). Situated algorithms: A sociotechnical systemic approach to bias. Online Information Review, 44(2), 325-342.
Duberry, J. (2022). Artificial intelligence and democracy: Risks and promises of AI-mediated citizen–government relations. In Artificial intelligence and democracy. Edward Elgar Publishing.
Frosio, G., & Geiger, C. (2023). Taking fundamental rights seriously in the Digital Services Act's platform liability regime. European Law Journal, 29(1-2), 31-77.
Gorwa, R., Binns, R., & Katzenbach, C. (2020). Algorithmic content moderation: Technical and political challenges in the automation of platform governance. Big Data & Society, 7(1), 2053951719897945.
Hunkenschroer, A., & Lutge, C. (2022). Ethics of AI-enabled recruiting and selection: A review and research agenda. Journal of Business Ethics, 178(3).
Karaboga, U., & Vardarlier, P. (2021). Examining the use of artificial intelligence in recruitment processes. Bussecon Review of Social Sciences, 2(4), 1-17.
Karim, M., Bhuiyan, A., Kumer, S., & Nath, D. (2021). Conceptual framework of recruitment and selection process. Journal of Business and Social Science Research, 11(2), 18-25.
Lundvall, H. (2022). Artificial intelligence in recruitment. Uppsala University, 3-57.
Meshram, R. (2023). The role of artificial intelligence (AI) in recruitment and selection of employees in the organization. Russian Law Journal, 11(9).
Miragoli, M. (2025). Conformism, ignorance & injustice: AI as a tool of epistemic oppression. Episteme, 22(2), 522-540.
Mupaikwa, E., & Yadav, P. (2024). Artificial intelligence for employee selection, recruitment, and training in business organizations. In Human resource strategies in the era of artificial intelligence (pp. 1-28).
Nawaila, M. B., Kanbul, S., & Ozdamli, F. (2018). A review on the rights of children in the digital age. Children and Youth Services Review, 94, 390-409.
Nawaz, N., Hemalatha, A., Pathi, B., & Gajenderan, V. (2024). The adoption of artificial intelligence in human resources management practices. International Journal of Information Management Data Insights, 4(1).
Odudu, Q. (2024). Technological solutions for protecting children from online predators: Current trends and future directions. Available at SSRN.
Parmar, H., & Murari, U. K. (2025). Human-AI synergy in ethical content moderation: Navigating fairness, accountability, and transparency challenges. In Ethical AI solutions for addressing social media influence and hate speech (pp. 191-212). IGI Global Scientific Publishing.
Peicheva, M. (2023). Data analysis from the applicant tracking system. Creative Space Association, 2, 6-15.
Pellegrino, A., & Stasi, A. (2024). A bibliometric analysis of the impact of media manipulation on adolescent mental health: Policy recommendations for algorithmic transparency. Online Journal of Communication and Media Technologies, 14(4), e202453.
Perez Vallejos, E., Dowthwaite, L., Creswich, H., Portillo, V., Koene, A., Jirotka, M., McCarthy, A., & McAuley, D. (2021). The impact of algorithmic decision-making processes on young people's well-being. Health Informatics Journal, 27(1), 1460458220972750.
Pfeiffer, M. J. (2023). First, do no harm: Algorithms, AI, and digital product liability. arXiv preprint arXiv:2311.10861.
Polli, F. (2019). Using AI to eliminate bias from hiring. Harvard Business Review.
Rathore, S. (2023). The impact of AI on recruitment and selection processes: Analysing the role of AI in automating and enhancing recruitment and selection procedures. International Journal for Global Academic & Scientific Research, 2(2), 78-93.
Solek-Borowska, C., & Wilczewska, M. (2018). New technologies in the recruitment process. Economics and Culture, 15(2), 25-33.
St-Jean, E. (2022). Applicant tracking system (ATS). Tech Target.
Subrahmanyam, S. (2025). Psychological impact of AI moderation. In Content moderation in the age of AI (pp. 191-220). IGI Global Scientific Publishing.
Thakur, A., Hinge, P., & Adheraonkarq, V. (2023). Use of artificial intelligence (AI) in recruitment and selection. SSRN Electronic Journal.
Tilcsik, A. (2021). Statistical discrimination and the rationalization of stereotypes. American Sociological Review, 86(1), 93-122.
Turdialiev, M. A. (2024). Comparative analysis of different countries on regulation of AI in social relations. Central Asian Journal of Social Sciences and History, 5(4), 203-208.
Ul Oman, Z., Siddiqua, A., & Noorain, R. (2024). Artificial intelligence and its ability to reduce recruitment bias. World Journal of Advanced Research and Review, 24(1), 551-564.
Vedapradha, R., Hariharan, R., & Shivakami, R. (2019). Artificial intelligence: A technological prototype in recruitment. Journal of Service Science and Management, 12(3), 382-390.
Wang, C., Yang, Z., Li, Z. S., Damian, D., & Lo, D. (2024). Quality assurance for artificial intelligence: A study of industrial concerns, challenges and best practices. arXiv preprint arXiv:2402.16391.
Wright, J., & Arkinson, D. (n.d.). The impact of artificial intelligence within the recruitment industry: Defining a new way of recruiting. Carmichael Fisher.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Sarah Marzooq
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
