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How AI Systems Work – Practical Foundations for Modern Learners

Lesson 7: Uncertainty, Causality, and Better Questions

Lesson Objectives

By the end of this lesson, students should be able to:

  • Explain uncertainty as incomplete or imperfect knowledge.
  • Tell the difference between correlation and causation.
  • Ask better questions when AI output relies on assumptions.

Lesson Content

Real situations are uncertain. The system may not know all facts. The data may be incomplete. The future may change. A good AI answer should respect uncertainty instead of pretending everything is known.

Probability helps describe what may be likely. Causality asks a different question: what would happen if something changed? Two things can move together without one causing the other. For example, a business may see more website visits and more sales in the same week. That does not prove the visits caused the sales. A sale, a promotion, a holiday, or another factor may explain both.

When students use AI for decisions, they should ask: What facts are known? What is assumed? What evidence supports the answer? What would change the recommendation?

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