What is model explainability architecture in classification systems?

Model explainability architecture provides insights into why classification models make specific predictions.

Explainability systems integrate post-hoc explanation tools like SHAP, LIME, and attention visualization into ML pipelines. These systems often run parallel to inference pipelines and generate feature attribution scores. In regulated industries, explainability is mandatory for compliance and auditability.