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職務の関連性、代理人のバイアス、グループの評価、記録の修正、人間によるレビュー、文書化、および管轄区域固有の法律をテストします。

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質問 1 の 8

An HR model ranks candidates. What should the ranking be treated as?

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クイズに答えると、答えと解説が表示されます。

  1. An HR model ranks candidates. What should the ranking be treated as?
    • A final measure of a candidate’s worth
    • Proof that an applicant will succeed
    • A recommendation that requires job-related human review, not a neutral fact about a person
    • A legally complete decision record
  2. Why can removing race and gender fields fail to remove bias?
    • Models cannot use indirect signals
    • Removing two fields guarantees fairness
    • Bias only enters through labels
    • Other variables may act as proxies or reflect historical patterns
  3. Which question should precede using AI to screen applicants?
    • What job-related outcome is being predicted, and who makes the final decision?
    • How many applicants can be rejected automatically?
    • Can the vendor hide its thresholds?
    • Should prior hiring outcomes be copied unchanged?
  4. A model performs well overall but poorly for applicants needing an accommodation. What is needed?
    • Ignore the difference because aggregate accuracy is high
    • Evaluate the complete application and review process across relevant groups and accommodations
    • Remove accommodation requests from the records
    • Use the score as the only criterion
  5. A candidate sees an incorrect work-history record in an automated screening process. Which safeguard matters?
    • A higher model threshold only
    • A longer automated assessment
    • A second score from the same data
    • A meaningful way to correct inaccurate records
  6. How should prior hiring decisions be used as model labels?
    • Assume they are objective ground truth
    • Use them without review because they are historical
    • Check whether they reflect relevant qualifications and outcomes before reuse
    • Replace them with the model’s predictions
  7. What should a vendor evaluation document?
    • Only product branding
    • Data sources, model versions, thresholds, and decisions
    • Only the number of employees screened
    • A promise of unbiased results
  8. Which current legal practice is safest when using AI for hiring?
    • Consult applicable law for the jurisdiction and specific use
    • Assume one global rule applies everywhere
    • Rely on the model to determine legality
    • Ignore local accommodation requirements