Source, version and history design.
AI SYSTEM ACCOUNTABILITY / PUBLIC EXPLANATION
How should AI system accountability be designed?
Having a model explain itself in prose is not sufficient for accountability. The Japanese source combines authoritative references, versions, approvals, action records, reason codes and return-for-review conditions so people can inspect who decided what.
Do not rely on a model's explanation alone
An answer's rationale may be useful, but does not establish its source, evidence or the boundary of responsibility. Combine source references, versions, approval and activity records so the claims can be examined.
- Design aim: make an explanation checkable against separate reference materials.
Separate the responsible roles
Record the responsibilities of the AI, its operator, the approver and the party accountable for publication. Identify which decisions are automated and where a human's authorization is required.
- Design aim: make the accountability boundary and the reviewer visible.
Connect to authoritative public references
Link relevant claims to externally available source and specification pages when appropriate, while keeping internal information and public disclosures separate. Documentation in the public layer is not equivalent to disclosure of confidential evidence.
- Design aim: make the published account traceable without expanding access to private records.
Related sources and materials
Decision and stopping architecture.
Pre-publication review and audit-design context.
Metadata for semantic boundaries.
What action evidence should be recorded.
doc_id: C3-WEB-ANSWERS-AI-ACCOUNTABILITY-DESIGN-EN-0.1 / version: 0.1.0 / status: REVIEW_REQUIRED / translated: 2026-10-13 / source_doc_id: C3-WEB-ANSWERS-AI-ACCOUNTABILITY-DESIGN-0.1 / source_last_updated: 2026-06-05