Mohammed Mahshook

Auditing Machine-Learning Models and Their Training Data with Explainability and First-Principles Verification: Application to Spin Hall Conductivity

A model-agnostic audit protocol is introduced, combining SHAP attribution, counterfactual partial dependence analysis, and Rashomon-style cross-model verification, with every finding adjudicated by targeted density functional theory (DFT).