Authors : Pooja Hukkeri; Dr. Sanjivkumar Pol; Sainath Shivappa Kuratti

Volume/Issue : Volume 11 - 2026, Issue 8 - August

---

Google Scholar : https://tinyurl.com/2s4z7wuc

Scribd : https://tinyurl.com/mrx2nzxd

DOI : https://doi.org/10.38124/ijisrt/26aug499

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

---

Abstract : Artificial Intelligence (AI) is increasingly transforming recruitment, yet its ethical deployment within small and medium enterprises (SMEs) remains uncertain. This study investigates how ethical AI adoption influences trust formation in SME recruitment, emphasizing the mediating roles of fairness and transparency. Drawing upon Ethical Climate Theory and the Technology Acceptance Model, a quantitative, cross-sectional survey was conducted among 380 SME recruiters and HR managers across India.

Keywords : Ethical AI, Fairness, Transparency, Trust, SME recruitment, Responsible Technology, Ethical Climate theory and Technology Acceptance Model.

References :

1. Bogen, M., & Rieke, A. (2018). Help Wanted: An Examination of Hiring Algorithms, Equity, And Bias. Upturn Report. Https://Www.Upturn.Org/Reports/2018/Hiring-Algorithms/
2. Cowgill, B., Dell’Acqua, F., & Deng, S. (2023). Biased Programmers? Or Biased Data? A Field Experiment In Operationalizing AI Ethics In Hiring. Management Science, 69(2), 981–1003. Https://Doi.Org/10.1287/Mnsc.2022.4517
3. European Commission. (2021). Ethics Guidelines For Trustworthy AI. Brussels: High-Level Expert Group On Artificial Intelligence.
4. Floridi, L., & Cowls, J. (2022). A Unified Framework Of Five Principles For AI In Society. Harvard Data Science Review, 4(1). Https://Doi.Org/10.1162/99608f92.E0c3c0e8
5. Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2018). Fair, Transparent, And Accountable Algorithmic Decision-Making Processes. Philosophy & Technology, 31(4), 611–627. Https://Doi.Org/10.1007/S13347-017-0279-X
6. Lu, Y., Li, T., & Chen, X. (2023). Ethical AI And Organizational Trust: The Moderating Role Of Transparency In Human–Machine Collaboration. Journal Of Business Research, 155, 113394. Https://Doi.Org/10.1016/J.Jbusres.2022.113394
7. Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating Bias In Algorithmic Hiring: Evaluating Claims And Practices. Proceedings Of The 2020 Conference On Fairness, Accountability, And Transparency (FAT*), 469–481. Https://Doi.Org/10.1145/3351095.3372828
8. 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
9. Tzafestas, S. G. (2022). Ethics And Law In Autonomous Systems And Artificial Intelligence. AI & Society, 37(4), 1413–1429. Https://Doi.Org/10.1007/S00146-021-01201-5
10. Van Esch, P., Black, J. S., & Ferolie, J. (2021). Marketing AI Recruitment: The Next Phase In Job Application and Selection. Computers In Human Behavior Reports, 3, 100059. Https://Doi.Org/10.1016/J.Chbr.2021.100059
11. Wilson, H. J., Daugherty, P. R., & Bianzino, N. (2017). The Jobs That Artificial Intelligence Will Create. MIT Sloan Management Review, 58(4), 14–17.
12. Zhang, L., Zhao, Y., & He, Y. (2023). Trustworthy AI: Conceptual Foundations and Future Directions. Information Systems Frontiers, 25(5), 1913–1928. Https://Doi.Org/10.1007/S10796-022-10391-4
13. Magn. Japan, Vol. 2, Pp. 740-741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, P. 301, 1982].
14. M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science.