XAI Framework
A structured approach or set of guidelines that organizations use to implement, measure, and govern explainability practices across their AI systems.
Definition
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.
Real-World Example
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.
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