Explainable AI

Explanation and review patterns for AI-assisted decisions, including artifacts that support verification and audit.

Scope

Model output explanations intended for human review.
Traceability from input data to outputs and reason codes.
Evidence and logging suitable for internal control requirements.

Explanation types

Feature attribution or contribution summaries.
Rule-based reason codes (where applicable).
Counterfactual notes or sensitivity summaries (when supported).

Review workflow

Capture reviewer actions and outcomes as structured events.
Separate automated scoring from decision approval where needed.
Define escalation paths for exceptions and edge cases.

Operational controls

Version models and explanation methods together.
Log inputs/outputs with retention rules and access controls.
Monitor drift and review false positives/negatives in context.

Artifacts produced

Explainability outputs (reason codes, attribution summaries) designed for review.
Evaluation evidence describing explanation behavior and limitations.
Decision and review logs aligned to governance controls.

Used by: AI Personal Model
Connected pillars: Model Training & Data; Compliance & Ethics

AI Personal Model Model Training & Data Compliance & Ethics

CTA

Contact Maloni to discuss requirements, constraints, and next steps.