Advancing Recruitment Practices: Integrating Explainable AI for Transparent and Ethical Decision-Making

Abstract

The research focuses on improving transparency and equity in hiring by utilizing Explainable AI (XAI). Conventional AI-based hiring has been criticized for biased and inaccurate decisions. To address this, we propose a framework integrating LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) for clear insights into AI decision-making, effectively tackling the "black-box" issue. By combining LIME and SHAP, the framework offers transparent justifications for AI-generated choices using diverse data sources, such as resumes and social media profiles, to build a robust training dataset. Evaluation metrics like accuracy and recall confirm the reliability of these models in generating fair forecasts. This approach enhances the equity of hiring systems, building trust among recruiters and job seekers through explicit explanations of AI-driven decisions. Ultimately, incorporating Explainable AI addresses transparency concerns and advances ethical, accountable recruitment practices.

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