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Kuis Bias AI

Menilai bias AI melalui label, proksi, bukti subkelompok, kriteria keadilan, orang-orang yang terkena dampak, keputusan alur kerja, dan tinjauan berkelanjutan.

Jalur panduan terkaitBias AI
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Where can bias enter an AI system according to the guide?

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  1. Where can bias enter an AI system according to the guide?
    • Data, measurement, modeling choices, human judgments, and the surrounding workflow
    • Only through a sensitive column
    • Only through the user interface
    • Only after a model is deployed
  2. Why can removing a sensitive attribute fail to eliminate bias?
    • Sensitive attributes are always the only source
    • Other features or historical labels may encode related patterns
    • Removing columns guarantees fairness
    • Model evaluation no longer needs groups
  3. A dataset’s label reflects a past human decision. What should the team ask?
    • Whether the sensitive column was deleted
    • Whether the label is easy to predict
    • Whether the label captures the intended outcome or an old process
    • Whether the model has enough layers
  4. Why report sample sizes and uncertainty for subgroup metrics?
    • An average score is always sufficient
    • Every subgroup has the same sample size
    • Uncertainty makes group testing impossible
    • Small groups can have unreliable estimates and averages can hide disparities
  5. Why can choosing one fairness criterion leave other concerns unresolved?
    • One statistical criterion settles every legal concern
    • Different criteria answer different questions and may conflict
    • Any single metric guarantees fairness in every context
    • Choosing a metric removes the need for affected-community input
  6. Who should help select fairness criteria for a consequential system?
    • Only the engineering team
    • Only the model vendor
    • Domain experts and people affected by the system
    • A language model without review
  7. Why review the workflow around a model?
    • How predictions are used and challenged can change the distribution of harm
    • The model output fully determines fairness
    • Appeals cannot affect outcomes
    • Workflow has no effect after training
  8. What should a team do after applying a bias mitigation?
    • Hide subgroup results
    • Assume the mitigation certifies fairness
    • Stop monitoring
    • Evaluate its intended effect and possible new problems