Maswali yanayounganishwa na mwongozo · Ngumu Kiwango

Maswali ya Upendeleo wa AI

Tathmini upendeleo wa AI kupitia lebo, proksi, ushahidi wa kikundi kidogo, vigezo vya haki, watu walioathiriwa, maamuzi ya mtiririko wa kazi, na ukaguzi unaoendelea.

Njia za mwongozo zinazohusianaUpendeleo wa AI
Swali 1 ya 8

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

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  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