A Multi-Stage Framework for Bias Detection and Mitigation in AI-Driven Recruitment Systems
TL;DR
An integrated framework for bias detection and mitigation in AI-driven recruitment systems, combining interventions at the data, model, and decision levels within a unified evaluation pipeline is introduced, demonstrating that bias in recruitment systems arises from the interplay between data and model dynamics.
Abstract
The use of machine learning in recruitment has raised growing concerns about fairness, as automated hiring systems can generate unequal outcomes across demographic groups. These disparities are influenced not only by imbalanced data but also by the behavior of learning algorithms, making bias a multidimensional challenge that cannot be effectively addressed with single-stage solutions. This study introduces an integrated framework for bias detection and mitigation in AI-driven recruitment systems, combining interventions at the data, model, and decision levels within a unified evaluation pipeline. The framework is assessed using multiple classification models of varying complexity and evaluated with established fairness metrics. In addition, explainability techniques are employed using SHAP-based feature attribution to investigate hidden dependencies and assess the sensitivity of predictions to demographic attributes. Experimental results show that baseline models achieve strong predictive performance, with accuracy ranging from 0.807 to 0.816; however, fairness evaluation reveals substantial disparities, with Disparate Impact as low as 0.190 and Demographic Parity Difference exceeding 0.27 in some cases. After applying the proposed multi-stage mitigation approach, fairness metrics improve significantly: Disparate Impact meets or exceeds the legal threshold of 0.80 across all models, with reductions in Demographic Parity Difference of 70–85% and Equal Opportunity Difference of 47–75%, and demographic disparities are reduced to 0.029–0.053. These improvements are achieved with minimal performance trade-off, as overall accuracy decreases by at most 4.2 % points while ROC AUC remains unchanged. The findings demonstrate that bias in recruitment systems arises from the interplay between data and model dynamics and highlight the importance of coordinated mitigation strategies throughout the machine learning lifecycle. This work provides a practical, scalable approach to developing fair and transparent AI systems for hiring applications.