ガイド連動クイズ · 簡単 レベル

AI 意思決定クイズ

AI の決定が証拠、予測、アクション ルール、エラー コスト、延期、フィードバック、監視、修正パスをどのように分離するかをテストします。

関連するガイドパスAI の意思決定
質問 1 の 8

意思決定の段階を分ける順序はどれですか?

すべての質問をプレビューする8 質問

クイズに答えると、答えと解説が表示されます。

  1. 意思決定の段階を分ける順序はどれですか?
    • 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. モデルの最も可能性の高い予測が自動的に最適なアクションにならないのはなぜですか?
    • 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. 意思決定ワークフローは、いつ誤報と見逃されたイベントの両方を評価する必要がありますか?
    • 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. モデルスコアに基づいて動作するためのしきい値に影響を与えるものは何ですか?
    • 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. ワークフローによって一部のケースのレビューが延期されるのはなぜですか?
    • 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