SHAP-Based Bias-Weighting Methodology for Post-Hoc Calibration in Healthcare AI: A Defensive Publication

This defensive publication establishes prior art for a SHAP-based bias-weighting methodology applied to healthcare AI systems. The method provides a post-hoc calibration layer that quantifies and mitigates algorithmic bias using SHAP-derived feature attribution weights at inference time. Input vectors include clinical variables, protected demographic identifiers, and socioeconomic proxies. The system outputs a recalibrated risk probability score and adjusted classification, mathematically normalized to negate biased feature attributions and validated at 96% accuracy across demographic cohorts. Published to prevent third-party patent claims on this general methodology. Implemented in EquiPath by Cloak & Quill Research.