Bias Detection in Recruitment Algorithms Using AI Frameworks

Abstract

Recruitment algorithms are increasingly used to streamline hiring processes, but they can inadvertently perpetuate or amplify biases. This research introduces an advanced AI-driven framework for bias detection in recruitment algorithms, utilizing Explainable AI (XAI) to ensure interpretability. A pivotal feature analyzed is candidate demographic data, which may implicitly influence algorithmic decisions. Preprocessing involves synthetic data balancing through SMOTE (Synthetic Minority Oversampling Technique) to mitigate class imbalances and ensure fairness. Classification is conducted using a Random Forest classifier, chosen for its robustness and capability to highlight feature importance. The proposed framework effectively identifies and quantifies biases, paving the way for more equitable recruitment processes.