Cuestionario vinculado a una guía · fácil Nivel

Prueba de toma de decisiones de IA

Pruebe cómo las decisiones de IA separan la evidencia, las predicciones, las reglas de acción, los costos de error, el aplazamiento, la retroalimentación, la supervisión y las rutas de corrección.

Rutas de guía relacionadasToma de decisiones con IA
Pregunta 1 de 8

Which sequence separates the stages of a decision?

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Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.

  1. Which sequence separates the stages of a decision?
    • Observed evidence, model estimate, action rule, accountable decision-maker
    • Model name, vendor, interface color, release date
    • Training data, model output, automatic approval in every case
    • Policy statement, confidence score, and no review
  2. Why is a model’s most likely prediction not automatically the best action?
    • Predictions cannot be measured
    • A prediction is not a policy; goals, constraints, and consequences also matter
    • Every action must maximize model confidence
    • A human cannot use predictions
  3. When should a decision workflow evaluate both false alarms and missed events?
    • Only when both have identical costs
    • Only after removing all human review
    • When either error can produce consequences that affect the action policy
    • Never; overall accuracy captures every consequence
  4. What can influence a threshold for acting on a model score?
    • Only the color of the dashboard
    • The number of people who built the model
    • The model’s marketing category
    • The consequences of errors, available capacity, and score reliability
  5. Why might a workflow defer some cases for review?
    • It can provide an alternative when consequences, capacity, or score reliability make immediate action unsuitable
    • Deferral guarantees a correct result
    • It removes accountability
    • It proves the prediction is causal
  6. What can happen if future training data records outcomes only for cases a system selected?
    • The system automatically becomes causal
    • Those data may reflect past system choices rather than the full population
    • The model can no longer produce scores
    • All unselected cases become confirmed errors
  7. What should accompany consequential decisions according to the guide?
    • A generic confidence statement alone
    • An unexplained automated outcome
    • Expert oversight, evidence-grounded explanations, and a way to correct mistakes
    • A guarantee that the model never errs
  8. What should a team test before relying on a consequential workflow?
    • Only the model in isolation
    • Only whether the interface opens
    • A different task with the same vendor
    • The complete workflow under the conditions where it will be used