Use sensitive attributes to evaluate fairness for a small group.
Protect the data and prevent reidentification.
Choose two designs.
Limit purpose, access, and retention, use aggregation or anonymization, and share results at a level that does not identify small groups.
Detailed explanation
The result itself is less likely to expose people.
Fairness measurement and privacy are bounded together.
Evaluation data can become a privacy breach.
Harm cannot be detected.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 4.1・5.2の公平性評価、プライバシー、データ最小化を確認する。
Official references
What to confirm
公平性を測るための属性データも保護対象として設計できる。
Key points
- Purpose limitation
- Anonymization
- Reidentification
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
Re-identification risk
The possibility that an individual can be identified by combining released data with other information.
Purpose limitation
Using personal data only for the specific purposes that were defined and approved.