Define standardized formats for explanations, feature attributions, and interpretation metadata. Support multiple explanation types: local (single prediction), global (model behavior), and counterfactual explanations.
ExplanationFormat โข AttributionVector โข ExplanationType
Implement core explainability algorithms including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), attention mechanisms, integrated gradients, and decision tree extraction.
SHAP โข LIME โข Attention โข IntegratedGradients
Establish protocols for requesting, generating, and validating explanations. Define trust metrics including fidelity, consistency, stability, and comprehensibility scores to measure explanation quality.
ExplanationRequest โข TrustMetrics โข ValidationProtocol
Integrate XAI capabilities into existing ML pipelines and provide visualization tools for decision-makers, regulators, and end-users. Support interactive exploration, audit trails, and compliance reporting.
MLIntegration โข VisualizationAPI โข AuditTrail