AI and Machine Learning You Can Explain
Use techniques like SHAP, counterfactuals, and partial dependence to show why predictions change. Stakeholders gain confidence when drivers are visible and consistent. Comment with your toughest explainability challenge; we may demo solutions in a follow-up.
AI and Machine Learning You Can Explain
Monitor data distribution shifts, score stability, and subgroup performance. Document assumptions, approvals, and retraining triggers. Clear governance prevents silent degradation and regulatory trouble. Subscribe for our practical model risk control templates.