Use an AI-assisted lending decision system.
Users need to understand the purpose, limits, and source of a result.
Which activity improves transparency and explainability?
Document the purpose, input data, limitations, human involvement, and a contact or appeal path for users.
Detailed explanation
The automation boundary and accountability become unclear.
Users can understand the system's role and seek human review when necessary.
Without records, explanations and audits cannot be performed.
This encourages overreliance and prevents informed risk decisions.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 4.2で、透明性・説明可能性・監査の説明を確認する。
Official references
What to confirm
説明可能性を、モデルの内部だけでなく利用者への情報提供と責任分担まで含めて説明できる。
Key points
- Transparency
- Stating limitations
- Accountability
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Compare the definition with the AIF-C01 objective and the service documentation before applying it.
- Keep the concept separate from a particular answer choice or implementation step.
Foundation review
Audience fit
Technical documentation and user-facing explanations may differ, but both should support informed use.
Recourse
People affected by an automated result need a way to request review or correction where appropriate.