A hiring model still shows regional disparities after removing location.
Investigate fairness rather than assuming the problem is solved.
Choose two appropriate investigations.
Check whether remaining features proxy for protected attributes and monitor group-specific errors and impacts.
Detailed explanation
Postal codes or behavior may encode the removed attribute indirectly.
Postal codes or behavior may encode the removed attribute indirectly.
The operational consequence may be hidden by overall averages.
The operational consequence may be hidden by overall averages.
Proxy variables and sampling or label bias may remain.
Proxy variables and sampling or label bias may remain.
Averages can hide group harm.
Averages can hide group harm.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01ガイドDomain 4.1のバイアス、公平性、データ品質を確認する。Expected result
代理変数とグループ別影響を確認する理由を説明できる。Key points
- Proxy
- Group metrics
- Impact
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
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.