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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 05

Published on: May 2026

THE ALGORITHMIC MIRROR: CAN ARTIFICIAL INTELLIGENCE TRULY MITIGATE HUMAN BIAS IN HIRING AND PERFORMANCE MANAGEMENT

Sonny B. Kollio Sam Siryon

DOI:https://doi.org/10.55041/ijcope.v2i5.538

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Plagiarism Passed Peer Reviewed Open Access

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Abstract

Artificial Intelligence (AI) is increasingly marketed as a neutral arbiter capable of eliminating unconscious bias from human resource processes, with the global HR technology market expected to expand from USD 43.7 billion in 2025 to over USD 81 billion by 2032. However, emerging evidence indicates that algorithms often inherit and amplify the historical biases present in training data. This study examines the dual role of AI in the workplace: as a tool for bias reduction and as a potential vehicle for systemic discrimination. Drawing on empirical studies from 2024–2026, this paper analyses three primary vectors of AI bias in hiring, data bias, interaction bias, and evaluation bias, and evaluates contemporary mitigation frameworks.

KEYWORDS

Algorithmic Bias, AI Ethics, HR Analytics, Diversity and Inclusion, Predictive

Hiring, Fairness in Machine Learning, Human-in-the-Loop System

How to Cite this Paper

Kollio, S. B. & Siryon, S. (2026). The Algorithmic Mirror: Can Artificial Intelligence Truly Mitigate Human Bias in Hiring and Performance Management. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.538

Kollio, Sonny, and Sam Siryon. "The Algorithmic Mirror: Can Artificial Intelligence Truly Mitigate Human Bias in Hiring and Performance Management." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.538.

Kollio, Sonny, and Sam Siryon. "The Algorithmic Mirror: Can Artificial Intelligence Truly Mitigate Human Bias in Hiring and Performance Management." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.538.

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References

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[4] M. Kaya and T. Bogers, "Human, algorithm, or both? Gender bias in human-augmented recruiting," arXiv preprint arXiv:2603.06240, 2026. [Online]. Available: https://arxiv.org/abs/2603.06240

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[6] P. K. Parekh and S. D. Shetty, "Detecting and mitigating algorithmic bias in automated hiring systems using fairness constraints," Semantic Scholar, 2025. [Online]. Available: https://www.semanticscholar.org/author/Parina-K.-Parekh/2421838037

[7] A. Peña et al., "Addressing bias in LLMs: Strategies and application to fair AI-based recruitment," arXiv preprint arXiv:2506.11880, 2025. [Online]. Available: https://arxiv.org/abs/2506.11880

[8] RAIL Research Team, "AI hiring bias: Real cases, legal consequences, and prevention," Responsible AI Labs, Nov. 7, 2025. [Online]. Available: https://responsibleailabs.ai/knowledge-hub/articles/ai-hiring-bias-legal-cases

Ethical Compliance & Review Process

  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
  • •Authors retain copyright.
  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: May 18 2026
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