Explainable AI means stakeholders can see why a system recommended or took an action—linked to sources, policies, and workflow steps—not only the final natural-language answer.

Interpretable ML focuses on model internals for data scientists. Explainable AI for business focuses on operational transparency—citations, policies applied, approvers, and system actions—that CFOs, compliance, and internal audit can review without reading model weights.

Ask for a live replay of a past decision, how source documents are cited, what happens when two policies conflict, and whether explanations are stored in the audit trail export. Avoid accepting generic 'trust scores' without business context.

Glass Box approaches embed explanation into the workflow—each step shows inputs, rules, and outcomes—rather than bolting a summary onto a black-box answer. That supports both user trust and audit reconstruction.

Legal interpretation belongs with your counsel. Operationally, firms handling personal data benefit from systems that log purpose, access, and decision rationale. Explainability features support accountability discussions; they do not by themselves mean legal compliance.