Explanation Policy

Defines the precise moments in a decision lifecycle when an explanation must be generated. This is not always post-hoc; policies can mandate pre-decision justifications for high-stakes scenarios.

• On-Request: Generated only when a user explicitly asks 'Why?'.
• Always-On: Automatically surfaced with every prediction.
• Threshold-Based: Triggered when confidence scores fall below a defined boundary or when a decision is adverse.

Dictates the structural output of the rationale to match the consumer's technical literacy. A policy must resolve the tension between faithful (mathematically accurate) and plausible (human-readable) explanations.

• Natural Language: Free-text justifications for end-users.
• Feature Attribution: Numerical weights (e.g., SHAP values) for data scientists.
• Contrastive: 'Why A instead of B' formats for recourse scenarios.

Maps specific explanation types to distinct user roles. A compliance officer requires different information than the subject of the decision.

• User-Adaptive Explanations: Tailoring complexity based on technical expertise.
• Regulatory Personas: Generating specific outputs designed to satisfy GDPR Right to Explanation mandates.
• Developer Debugging: Exposing internal model state and activation vectors for engineering teams.

Establishes the minimum acceptable faithfulness of a rationale. The policy must explicitly state whether the system prioritizes true mechanistic reasoning or plausible storytelling.

• Faithful Rationales: Strict requirement that the explanation mirrors the model's actual computation.
• Plausible Rationales: Acceptable for low-stakes scenarios where user trust is the primary goal.
• Hallucination Detection: Mandatory guardrails to flag fabricated justifications.

Rules governing how explanations link claims to evidence. This prevents 'unsupported rationalization' by enforcing strict source grounding.

• Evidence Attribution: Pointing to specific input features or training data clusters.
• Citation Generation: Requiring references to external documents in RAG-based systems.
• Factual Consistency: Verifying that the rationale does not contradict the source material.

Defines whether the explanation must provide a path to a different outcome. This transforms the policy from a passive audit tool into an active user interface.

• Counterfactual Rationales: Specifying the minimal changes needed to flip the decision.
• Causal Rationales: Requiring cause-and-effect logic rather than mere correlation.
• Minimal Sufficient Explanations: Providing the shortest possible justification that fully explains the outcome.

The regulatory requirement under the General Data Protection Regulation for providing meaningful information about the logic involved in automated decisions. An explanation policy operationalizes this legal mandate by defining the technical standard for 'meaningful.'

• Requires safeguards for solely automated decisions with legal effects
• Recital 71 explicitly mentions the right 'to obtain an explanation of the decision reached'
• Drives the need for counterfactual explanations in consumer finance and hiring

Quantitative measures designed to automatically score how accurately a generated explanation reflects the model's internal decision process. An explanation policy must specify a minimum faithfulness threshold to prevent plausible-sounding but misleading rationales.

• Sufficiency: Does the explanation alone cause the same prediction?
• Comprehensiveness: Does removing explained features change the output?
• Policies often mandate a faithfulness floor of 0.85 for high-stakes domains

Rationales dynamically tailored to the technical expertise, role, or specific needs of the individual end-user. An explanation policy defines persona-based templates that control the complexity and format of justifications.

• A data scientist sees SHAP value plots
• A loan applicant sees a natural language counterfactual: 'If your income were $5k higher, you would be approved'
• A compliance officer sees a full audit trail with source grounding

Rationales that not only explain a decision but also provide the user with clear steps to change the outcome in the future. An explanation policy should mandate recourse generation for any adverse automated decision.

• Must identify minimal sufficient changes to flip the prediction
• Example: 'To qualify, reduce your debt-to-income ratio by 12%'
• Directly addresses the 'right to recourse' implied in modern AI regulations

Structured transparency artifacts documenting a model's intended use, evaluation results, and limitations. An explanation policy often requires model cards as a precondition for deployment, providing a static, high-level explanation of system behavior.

• Includes intended use cases and out-of-scope applications
• Reports disaggregated evaluation results across demographic groups
• Serves as the 'cover sheet' for the dynamic explanations generated at runtime

Techniques used to identify and flag generated explanations that contain fabricated, nonsensical, or unfaithful information. A robust explanation policy requires real-time hallucination guards before any rationale is surfaced to a user.

• Factual consistency checks against source data
• Self-consistency sampling: generating multiple rationales and checking for agreement
• Policies define escalation paths when hallucination scores exceed a defined threshold