Ethical Use of AI in Continuous Recruitment: An Analysis of Algorithm Bias towards Candidates from Marginalized Backgrounds

https://doi.org/10.59971/jumper.v2i7.592

Authors

  • Welimas Kristina Parinsi Program Studi Manajemen, Stiem Bongaya, Makassar, Indonesia
  • Anugrah Dewi Lestari Musa Study Program Management, Sekolah Tinggi Ilmu Ekonomi Bongaya, Makassar, Indonesia
  • Kartika Septiary Pratiwi Musa Study Program Management, Universitas Negeri Makassar, Indonesia

Keywords:

Artificial Intelligence, Algorithmic Bias, Ethical Recruitment, Marginalized Communities, Qualitative Research

Abstract

This study explores the ethical implications of using Artificial Intelligence (AI) in continuous recruitment systems, with a specific focus on algorithmic bias against candidates from marginalized backgrounds in Makassar, Indonesia. Through a qualitative approach involving semi-structured interviews with HR practitioners, developers, and job seekers, the research reveals a concerning gap between technological advancement and ethical accountability. Participants from marginalized groups reported experiences of exclusion and invisibility, often without any transparency or feedback in the recruitment process. Meanwhile, most HR professionals and developers lacked awareness of how algorithmic models could replicate societal inequalities. The findings suggest that AI systems, if left unchecked, risk reinforcing discrimination rather than fostering equal opportunity. However, the study also uncovers a growing willingness among local stakeholders to engage in ethical reform and collaborative efforts toward more inclusive AI design. This research contributes to the discourse on fairness and accountability in digital hiring practices, offering actionable insights for socially responsible AI integration.

Downloads

Download data is not yet available.

References

Binns, R. (2020). On the apparent conflict between individual and group fairness. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT*), 514–524. https://doi.org/10.1145/3351095.3372864

Cowgill, B., Dell’Acqua, F., & Deng, S. (2021). Biased programmers? Or biased data? A field experiment in operationalizing AI ethics (Columbia Business School Research Paper No. 3668622). https://doi.org/10.2139/ssrn.3668622

Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications.

Denzin, N. K., & Lincoln, Y. S. (2018). The SAGE handbook of qualitative research (5th ed.). SAGE Publications.

European Commission. (2022). Ethics guidelines for trustworthy AI. https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence

Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.

Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.

OECD. (2021). OECD recommendation on artificial intelligence. OECD Legal Instruments. https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449

Raji, I. D., Smart, A., White, R. N., Mitchell, M., Hutchinson, B., Gebru, T., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT*), 33–44. https://doi.org/10.1145/3351095.3372873

Tracy, S. J. (2020). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact (2nd ed.). Wiley-Blackwell.

Published

2025-03-28

How to Cite

Parinsi, W. K., Musa, A. D. L., & Musa, K. S. P. (2025). Ethical Use of AI in Continuous Recruitment: An Analysis of Algorithm Bias towards Candidates from Marginalized Backgrounds. Journal Management & Economics Review (JUMPER), 2(7), 215–220. https://doi.org/10.59971/jumper.v2i7.592

Issue

Section

Articles