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Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Vol 10 No 5 (2026): Volume 10, Issue 5, May 2026 | Pages: 569-579
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 27-05-2026
The accelerating integration of Artificial Intelligence into recruitment workflows has introduced measurable gains in efficiency, yet simultaneously raises significant fairness concerns rooted in biased historical training data. Hiring models frequently encode demographic preferences that disadvantage candidates on the basis of gender, geographic region, or educational background, often without any visible indication to the organizations deploying them. This paper presents FAIRHIRE, a full-stack intelligent auditing platform engineered to detect, quantify, explain, and report algorithmic bias embedded within AI-driven hiring systems. The framework accepts candidate hiring datasets in CSV format and evaluates decision fairness using four complementary metrics: Disparate Impact (DI), Statistical Parity Difference (SPD), Equal Opportunity Difference (EOD), and Average Odds Difference (AOD). An Explainable AI layer incorporating SHAP for global feature attribution and LIME for candidate-level decision transparency is integrated into the pipeline to illuminate the drivers of biased predictions. Based on these findings, the system autonomously produces prioritized remediation recommendations and generates professional PDF audit reports aligned with EEOC compliance standards. The platform is implemented using React, Tailwind CSS, FastAPI, PostgreSQL, Redis, IBM AIF360, SHAP, LIME, and Docker, producing a deployment-ready, modular solution. Experimental evaluation on a synthetic dataset of 2,000 candidate records confirmed the framework's ability to identify HIGH-risk bias conditions, with gender and education Disparate Impact values of 0.712 and 0.679 respectively, both falling below the legally recognized 0.8 threshold. FAIRHIRE demonstrates a practical path toward transparent, accountable, and ethically governed AI recruitment infrastructure.
Algorithmic Fairness, Bias Detection, AI Hiring Systems, Explainable AI, SHAP, LIME, Disparate Impact, AIF360, Ethical AI, Recruitment Analytics.
Kornepati Varshitha, Kethavath Rajesh, & S Vijaya Lakshmi. (2026). FAIRHIRE: An AI Bias Detection and Fairness Evaluation Framework for Automated Hiring Systems. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(5), 569-579. Article DOI https://doi.org/10.47001/IRJIET/2026.105077
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