Practice Explainable AI (XAI) in Engineering - 32.11.1 | 32, AI-Driven Decision-Making in Civil Engineering Projects | Robotics and Automation - Vol 3
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32.11.1 - Explainable AI (XAI) in Engineering

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does XAI stand for?

💡 Hint: Think about AI and how we can explain it.

Question 2

Easy

Name one challenge of XAI.

💡 Hint: What makes explaining AI models difficult?

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does XAI aim to achieve?

  • Make AI models fully autonomous
  • Improve transparency of AI decisions
  • Increase AI complexity

💡 Hint: Think about why users need transparency in AI.

Question 2

True or False: SHAP is a method used to enhance AI's accuracy.

  • True
  • False

💡 Hint: Consider what SHAP does imply about explanations.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Given a dataset on maximum load tolerances, use SHAP to interpret how different factors impact AI predictions.

💡 Hint: Focus on interpreting why specific features are more critical than others.

Question 2

Critically analyze a case study where XAI improved trust in an engineering project. Identify key factors that led to this improvement.

💡 Hint: Consider which attributes made the AI's performance understandable.

Challenge and get performance evaluation