AI Personal Model

Provide a consistent architecture for producing model outputs alongside explanations, versioning, and evidence that supports review.

• Input types: structured records, text, and optional image/sensor signals.
• Processing: validation, normalization, feature computation, versioning.
• Outputs: scores/labels, explanations, and audit logs.

• Explanation outputs: feature attributions, rules, or counterfactual notes.
• Human review: reason codes and reviewer actions are captured.
• Audit readiness: versioned models and traceable input lineage.

• Controls: access control, encryption at rest/in transit, logging.
• Data retention: defined periods and deletion workflows.
• Access model: least privilege with role-based permissions.

• Policies: model approval criteria and change control.
• Evaluation: fairness/bias checks where applicable.
• Artifacts: model cards, data sheets, and review logs.

• Monitoring: drift, quality, and exception tracking.
• Updates: versioned rollouts with rollback strategy.
• Documentation: change logs and decision records.