Enhancing Recruitment Transparency and Efficiency with Explainable AI (XAI)
Abdelfattah Jamal, Karima Aissaoui, Sanae Kassal · 2024
In an era where data-driven decision-making is paramount, recruitment processes increasingly leverage advanced algorithms to enhance efficiency and effectiveness. However, the complexity and opacity of these machine learning models often create difficulties in understanding and trusting their predictions. This paper explores how Explainable AI (XAI) can bridge this gap, specifically based on two key techniques: Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). By applying these XAI methods to recruitment data, we aim to unravel the underlying factors influencing hiring decisions and improve the transparency of the process.