HIGH
AI Transparency & Explainability Reporting
Regulators, auditors, and end users increasingly demand human-readable explanations of how AI systems reach their decisions, particularly in high-stakes domains like finance, healthcare, hiring, and insurance. Effective transparency goes beyond technical model interpretability to include clear documentation of data sources, decision factors, confidence levels, and limitations that non-technical stakeholders can understand. When evaluating vendors, assess their support for multiple explanation methods (feature attribution, counterfactual, natural language), customizable report templates for different audiences (regulators, customers, internal audit), real-time explainability for production decisions, and compliance with emerging explainability standards in the EU AI Act and NIST AI RMF.