Detecting and Mitigating Algorithmic Discrimination in Recruitment Platforms

Abstract: The rapid adoption of artificial intelligence (AI) in recruitment has introduced efficiency gains but also raised concerns over fairness and bias. This study investigates the presence and mitigation of algorithmic discrimination in recruitment platforms through a data-driven and model-agnostic framework. Utilizing both synthetic and real-world recruitment datasets, the research applies logistic regression and random forest classifiers to detect disparities in hiring outcomes based on sensitive attributes such as gender and ethnicity. Fairness metrics—including Statistical Parity Difference (SPD) and Equal Opportunity Difference (EOD)—are employed to quantify bias before and after applying mitigation techniques. Among the techniques tested, adversarial debiasing demonstrated the most effective balance between fairness enhancement and predictive performance. Regression analysis revealed statistically significant negative coefficients for female and minority candidates, confirming bias in model decision-making. Post-mitigation results showed substantial improvement in fairness metrics (SPD improved from -0.28 to -0.05), with minimal compromise on accuracy. The findings underscore the necessity of integrating algorithmic audits and fairness-aware learning into AI-driven hiring systems. These insights contribute to the development of transparent, equitable recruitment platforms aligned with ethical AI principles and regulatory requirements. Keywords: algorithmic discrimination, recruitment platforms, fairness metrics, statistical parity, adversarial debiasing, AI ethics, bias mitigation, logistic regression, equal opportunity, algorithmic accountability