Citation:

Tasriqul Islam, Sadia Afrin, 2023. "Mitigating AI Bias in Recruitment: Policy Approaches for Transparent Candidate Selection and Broader Implications for Trust in Algorithmic Decisions," ESP Journal of Engineering & Technology Advancements 3(3): 116-125.

Abstract:

The hiring process determines the company's production and culture by finding qualified and compatible candidates. Industrial-organizational psychology and human resources experts have posted job postings, given exams to assess aptitude, and interviewed to determine compatibility for over a century. Big data and ML have replaced human recruiters with AI in several companies. AI systems' widespread use in recruiting has raised concerns that they may be biased and have an outsized impact if employed systematically. In the previous decade, AI fairness research has increased because of chatbot and candidate assessment algorithm bias. Using explainable artificial intelligence (XAI) to make ethical decisions and boost stakeholder trust is a hot issue in recruiting. This thesis applies XAI models to the employment process to provide a transparent and consistent decision-making framework that reduces biases and addresses AI-driven system ethics. AI will help HR and recruiting managers understand candidates and make more accurate quality assessments, according to the report. The research offers significant data to the continuing discourse about improving recruitment procedures, which should lead to more efficient and effective hiring. XAI may increase HR and recruiting manager data quality and applicant assessments, according to the study. It also considers the practicalities of XAI, its effects on recruiting, and the ethical issues surrounding its usage in organizations.

References:

[1] Athanasiou, M. N. (2023). Automation of recruitment: Decision-making system design and review of legal implications. DIVA. https://www.diva-portal.org/smash/record.jsf?dswid=2511&pid=diva2%3A1806249

[2] Bansal, A., K, D. A., S. Gangadharan, R, R. R., Ismail, M. M., & Assi, K. (2023). Ethical Considerations of AI Implementation in Business Planning: Ensuring Fairness and Transparency. Https://Ieeexplore.ieee.org/Abstract/Document/10465461, 9(09). https://doi.org/10.1109/icses60034.2023.10465461

[3] Hilliard, A., Guenole, N., & Leutner, F. (2022). Robots are judging me: Perceived fairness of algorithmic recruitment tools. Frontiers in Psychology, 13(09). https://doi.org/10.3389/fpsyg.2022.940456

[4] Hunkenschroer, A. L., & Kriebitz, A. (2022). Is AI recruiting (un)ethical? A human rights perspective on the use of AI for hiring. AI and Ethics, 3(1), 199–213. https://doi.org/10.1007/s43681-022-00166-4

[5] Langer, M., König, C. J., Back, C., & Hemsing, V. (2022). Trust in Artificial Intelligence: Comparing Trust Processes between Human and Automated Trustees in Light of Unfair Bias. Journal of Business and Psychology, 38(08). https://doi.org/10.1007/s10869-022-09829-9

[6] Li, L., Lassiter, T., Oh, J., & Lee, M. K. (2021). Algorithmic Hiring in Practice. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 09(9), 166–176. https://doi.org/10.1145/3461702.3462531

[7] Kassir, S., Baker, L., Dolphin, J., & Polli, F. (2022). AI for hiring in context: a perspective on overcoming the unique challenges of employment research to mitigate disparate impact. AI and Ethics, 3(6). https://doi.org/10.1007/s43681-022-00208-x

[8] Mihaljević, H., Müller, I., Dill, K., Aysel Yollu-Tok, & Maximilian von Grafenstein. (2023). More or less discrimination? Practical feasibility of fairness auditing of technologies for personnel selection. AI & Society, 9(6). https://doi.org/10.1007/s00146-023-01726-w

[9] Mujtaba, D., & Mahapatra, N. (2023). Fairness in AI-Driven Recruitment: Challenges, Metrics, Methods, and Future Directions. https://arxiv.org/pdf/2405.19699

[10] Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y., & Tarba, S. Y. (2022). 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

[11] Rama, J. (2021). AI-Powered Decentralized Recruitment System on the Blockchain. Global Disclosure of Economics and Business, 10(2), 91–104. https://doi.org/10.18034/gdeb.v10i2.734

[12] Roemmich, K., Rosenberg, T., Fan, S., & Nazanin Andalibi. (2023). Values in Emotion Artificial Intelligence Hiring Services: Technosolutions to Organizational Problems. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW1), 1–28. https://doi.org/10.1145/3579543

[13] Schloetzer, J. D., & Yoshinaga, K. (2023). Algorithmic Hiring Systems: Implications and Recommendations for Organisations and Policymakers. YSEC Yearbook of Socio-Economic Constitutions, 09(9). https://doi.org/10.1007/16495_2023_61

[14] Tabassam, A., Yaqoob, G., Cuong, V. H., Syed, M., Shahzadi, A., & Asghar, F. (2023). The Ethical Implication of Using Artificial Intelligence in Hiring and Promotion Decisions. Journal of Management & Educational Research Innovation, 1(2), 1–15. https://doi.org/10.5281/zenodo.10066900

[15] Olaniyan, O. P. -, Elufioye, O. A., Okonkwo, F. C., Udeh, C. A., Eleogu, T. F., & Olatoye, F. O. (2022). AI-DRIVEN TALENT ANALYTICS FOR STRATEGIC HR DECISION-MAKING IN THE UNITED STATES OF AMERICA: A REVIEW. International Journal of Management & Entrepreneurship Research, 4(12), 607–622. https://doi.org/10.51594/ijmer.v4i12.674

Keywords:

Explainable AI, AI integration HR, Ethical AI framework, XAI Recruitment, AI Transparency in HR, XAI Recruitment, AI Recruitment, Recruitment Bias, Fairness, and Accountability.