Diana

Bias Evaluation Framework in AI-Powered Resume Classification Using DistilBERT with SHAP Explainability and Automated Fairness Flagging

This paper proposes a multi-layer bias audit framework for AI-powered resume screening combining DistilBERT classification, SHAP explainability, automated fairness flagging, and locally-deployed LLaMA 2 interpretation. The framework achieved 74.8% of accuracy, 76.6% of precision, 74.8% of recall, and 74.7% of F1-Score...