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AI · Theory

Explainable Artificial Intelligence(XAI)

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Theory

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Explainable Artificial Intelligence(XAI)

Same idea as XAI: humans must see a reason, especially in credit, hiring, health. Techniques range from simple models (decision trees you can read) to post-hoc explanations of black boxes. Pick simpler models when the law needs a story.

Output — idea / do / check for Explainable Artificial Intelligence(XAI). Fill those three in the viva.

Exam tip

When a tree beats a deep net: explainability.

Example

# Explainable Artificial Intelligence(XAI)
print("idea :", "Explainable Artificial Intelligence(XAI)")
print("do   : one tiny example on paper")
print("check: one failure case")

Output: idea / do / check for Explainable Artificial Intelligence(XAI). Fill those three in the viva.

Short notes

  • DefReadable reasons for a decision.
  • RuleHigh-stakes: credit / health.

Questions

1

Explain Explainable (XAI) as if you are teaching a junior — definition, then one example.

2

What does the example print, and what does that prove?

3

What mistake do freshers make with Explainable (XAI)?

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