Practice Explainable Ai (xai) In Engineering (32.11.1) - , AI-Driven Decision-Making in Civil Engineering Projects

Practice: Explainable AI (XAI) in Engineering

Lesson 32.11.132, AI-Driven Decision-Making in Civil Engineering Projects

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9 questions

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4 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does XAI stand for?
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Think about AI and how we can explain it.
Question 2.
Name one challenge of XAI.
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What makes explaining AI models difficult?
3Question 3.
What does XAI aim to achieve?
- Make AI models fully autonomous
- Improve transparency of AI decisions
- Increase AI complexity
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Think about why users need transparency in AI.
4Question 4.
True or False: SHAP is a method used to enhance AI's accuracy.
- True
- False
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Consider what SHAP does imply about explanations.
Question 5.
Given a dataset on maximum load tolerances, use SHAP to interpret how different factors impact AI predictions.
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Focus on interpreting why specific features are more critical than others.
Question 6.
Critically analyze a case study where XAI improved trust in an engineering project. Identify key factors that led to this improvement.
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Consider which attributes made the AI's performance understandable.

5 more questions in this section. Enroll free to practise them all.