Algorithmic Fairness and Ethical Frameworks for Artificial Intelligence in Corporate Talent Acquisition
Author(s): Prisha Kapoor, Vikram Sethi, Anjali Menon, Rahul Srinivasan
Affiliation: Department of Organizational Behavior, Management Development Institute, Gurgaon, India
Page No: 20-23-
Volume issue & Publishing Year: Volume 3, Issue 2, 2026-02-25
Journal: International Journal of Modern Engineering and Management | IJMEM
ISSN NO: 3048-8230
DOI:
Abstract:
The rapid integration of Artificial Intelligence (AI) in recruitment has promised unprecedented efficiency in screening high volumes of candidates. However, the emergence of "black-box" biases in automated ranking systems has raised significant ethical concerns regarding diversity and inclusivity. This research investigates the implementation of Ethical AI Frameworks within the recruitment strategies of 40 mid-to-large-scale firms. By examining the impact of "de-biasing" algorithms on candidate selection rates across gender and ethnic demographics, the study evaluates whether technological intervention can truly achieve meritocratic outcomes. Our findings indicate that firms utilizing "Transparency-First" AI protocols observed a 22% increase in the diversity of their shortlists compared to those using standard proprietary algorithms. The paper proposes a Strategic Governance Model that balances computational efficiency with ethical accountability, providing a roadmap for HR leaders to navigate the complexities of AI-driven talent management in the 2026 labor market.
Keywords:
AI Ethics; Algorithmic Bias; Talent Acquisition; Diversity and Inclusion (D&I); Human Resource Technology; Strategic HR Management; Ethical AI.
Reference:
[1] P. Kapoor and V. Sethi, "De-biasing the Digital Gatekeeper: Algorithmic Fairness in Indian Tech Sourcing," Journal of Human Resource Management, vol. 18, no. 1, pp. 45–60, Jan. 2026.
[2] A. Menon, "Explainable AI and the Opacity Problem in Talent Acquisition," Management Strategy Review, vol. 14, pp. 112–128, Oct. 2025.
[3] R. Srinivasan, "The Impact of Pedigree Bias on Algorithmic Shortlisting," Indian Journal of Organizational Behavior, vol. 20, no. 4, pp. 22–38, Sept. 2025.
[4] S. K. Gupta, "Socio-Technical Systems Theory in the Age of AI," Academy of Management Perspectives, vol. 12, no. 2, pp. 85–102, June 2025.
[5] N. Verma, "Feature Masking as a Tool for Meritocratic Hiring," HR Technology Quarterly, vol. 9, no. 3, pp. 201–215, Nov. 2024.
[6] J. Malhotra, "From Efficiency to Equity: The Evolution of ATS Systems," Business Ethics and Technology, vol. 15, pp. 65–80, Feb. 2025.
[7] T. K. Rao, "The Fairness Quotient: A New Metric for HR Analytics," Strategic HR Monthly, vol. 30, no. 1, pp. 30–45, Jan. 2026.
[8] D. Sharma, "Blind Audits and the False Rejection Rate in Automated Recruitment," Journal of Applied Psychology in Management, vol. 22, no. 2, pp. 110–125, May 2024.
[9] L. Fernandez, "Gender Dynamics in AI-Driven Keyword Weighting," International Journal of Diversity Management, vol. 8, pp. 195–210, Apr. 2025.
[10] M. Joshi, "The Cost of Quality Hire in Ethical vs. Standard Recruitment Models," Economics of Human Capital, vol. 14, no. 4, pp. 101–118, Dec. 2025.
[11] K. P. Nair, "LIME and SHAP: Tools for Explainability in Management Decision Making," Data Science for Business Review, vol. 45, no. 1, pp. 12–28, Jan. 2026.
[12] R. Bansal, "The Feedback Loop of Exclusion: A Case Study of Tier-2 Candidate Rejection," Management Development Reports, vol. 12, pp. 5–20, July 2024.
[13] S. Chatterje
[1] P. Kapoor and V. Sethi, "De-biasing the Digital Gatekeeper: Algorithmic Fairness in Indian Tech Sourcing," Journal of Human Resource Management, vol. 18, no. 1, pp. 45–60, Jan. 2026.
[2] A. Menon, "Explainable AI and the Opacity Problem in Talent Acquisition," Management Strategy Review, vol. 14, pp. 112–128, Oct. 2025.
[3] R. Srinivasan, "The Impact of Pedigree Bias on Algorithmic Shortlisting," Indian Journal of Organizational Behavior, vol. 20, no. 4, pp. 22–38, Sept. 2025.
[4] S. K. Gupta, "Socio-Technical Systems Theory in the Age of AI," Academy of Management Perspectives, vol. 12, no. 2, pp. 85–102, June 2025.
[5] N. Verma, "Feature Masking as a Tool for Meritocratic Hiring," HR Technology Quarterly, vol. 9, no. 3, pp. 201–215, Nov. 2024.
[6] J. Malhotra, "From Efficiency to Equity: The Evolution of ATS Systems," Business Ethics and Technology, vol. 15, pp. 65–80, Feb. 2025.
[7] T. K. Rao, "The Fairness Quotient: A New Metric for HR Analytics," Strategic HR Monthly, vol. 30, no. 1, pp. 30–45, Jan. 2026.
[8] D. Sharma, "Blind Audits and the False Rejection Rate in Automated Recruitment," Journal of Applied Psychology in Management, vol. 22, no. 2, pp. 110–125, May 2024.
[9] L. Fernandez, "Gender Dynamics in AI-Driven Keyword Weighting," International Journal of Diversity Management, vol. 8, pp. 195–210, Apr. 2025.
[10] M. Joshi, "The Cost of Quality Hire in Ethical vs. Standard Recruitment Models," Economics of Human Capital, vol. 14, no. 4, pp. 101–118, Dec. 2025.
[11] K. P. Nair, "LIME and SHAP: Tools for Explainability in Management Decision Making," Data Science for Business Review, vol. 45, no. 1, pp. 12–28, Jan. 2026.
[12] R. Bansal, "The Feedback Loop of Exclusion: A Case Study of Tier-2 Candidate Rejection," Management Development Reports, vol. 12, pp. 5–20, July 2024.
[13] S. Chatterjee, "Ethical Auditing Protocols for Mid-Scale Enterprises," Corporate Governance Review, vol. 19, no. 3, pp. 75–90, Oct. 2025.
[14] V. Singh, "Predictive Accuracy vs. Social Inclusivity in AI Hiring," Technology and Society, vol. 14, no. 2, pp. 120–135, June 2025.
[15] C. Menon, "Human-in-the-Loop: Redefining the Recruiter’s Role in 2026," Pacific HR Journal, vol. 11, no. 1, pp. 33–46, Jan. 2026.
e, "Ethical Auditing Protocols for Mid-Scale Enterprises," Corporate Governance Review, vol. 19, no. 3, pp. 75–90, Oct. 2025.
[14] V. Singh, "Predictive Accuracy vs. Social Inclusivity in AI Hiring," Technology and Society, vol. 14, no. 2, pp. 120–135, June 2025.
[15] C. Menon, "Human-in-the-Loop: Redefining the Recruiter’s Role in 2026," Pacific HR Journal, vol. 11, no. 1, pp. 33–46, Jan. 2026.