A structured approach or set of guidelines that organizations use to implement, measure, and govern explainability practices across their AI systems.
A formalized program - often comprising policy documents, technical standards, and process workflows - that prescribes when and how to apply explainability techniques, defines required explanation formats (e.g., textual, visual), sets roles (explanation owners, auditors), and establishes checkpoints (design review, pre-deployment validation, periodic audits). It ensures consistent, auditable XAI deployment aligned with organizational risk and regulatory requirements.
A healthcare provider adopts an XAI Framework that mandates: (1) all diagnostic models include local explanations for each prediction; (2) explanation fidelity must exceed 90% as measured by agreement with ground-truth feature influences; and (3) quarterly XAI audits validate that explanations remain accurate after model retraining.
