A model built from historical approvals has low recall for one group.
Investigate and improve the cause.
Choose two appropriate responses.
Inspect group counts, labels, missingness, and selection, then compare resampling, weighting, and label changes on performance and fairness.
Detailed explanation
Data-generation and measurement bias can be found.
Data-generation and measurement bias can be found.
Performance and fairness effects are both measured.
Performance and fairness effects are both measured.
The harm is hidden and information is lost.
The harm is hidden and information is lost.
Bias and proxy effects may remain.
Bias and proxy effects may remain.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 4.1のバイアス、データ品質、公平性対策を確認する。Expected result
偏りの原因をデータ生成過程まで追い、緩和策の副作用も評価できる。Key points
- Label quality
- Resampling
- Proxy
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.