Maswali yanayounganishwa na mwongozo · Ngumu Kiwango

Maswali ya Kurekebisha Uaminifu ya AI

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Njia za mwongozo zinazohusianaUrekebishaji wa Uaminifu wa AI
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What does statistical calibration describe?

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  1. What does statistical calibration describe?
    • Whether users like the interface
    • Whether the model ranks every example correctly
    • How often the model explains itself
    • Whether predicted probabilities match observed frequencies across comparable cases
  2. Can a statistically calibrated model still perform poorly?
    • No, calibration guarantees high accuracy
    • Yes; calibration and task quality are different properties
    • Only if users distrust it
    • No, if its confidence is displayed
  3. What does a model’s verbal statement “I am 95% sure” establish?
    • Only that it produced those words; the confidence needs empirical evaluation
    • That its output is calibrated
    • That the source evidence is correct
    • That a human should not review it
  4. A model is calibrated on one dataset. What remains uncertain after a domain shift?
    • Whether its old test score exists
    • Whether all users agree with the system
    • Whether the calibration still holds under changed language, domain, or task
    • Whether the interface color is accessible
  5. Which interface feature can support independent checking?
    • Use a decorative confidence badge without measurement
    • Show sources and relevant limitations beside the answer
    • Hide the source to reduce clutter
    • State that the model is always reliable
  6. How should teams evaluate human trust in the system?
    • Measure only whether users click “accept”
    • Assume explanations always improve trust
    • Count the model’s confident words
    • Test whether people notice errors and use overrides appropriately
  7. Why distinguish accuracy from calibration?
    • Accuracy and calibration are synonyms
    • Calibration only measures latency
    • A model may rank cases correctly while its probabilities are systematically too high or low
    • A calibrated score cannot be wrong
  8. What should happen when measured calibration changes over time?
    • Update user guidance and keep a route to report errors or complete the task another way
    • Leave the interface unchanged indefinitely
    • Remove all sources
    • Display a more precise confidence value