Practice Local (2.2) - Explainable AI (XAI) and Model Interpretability

Practice: Local

Lesson 2.2Explainable AI (XAI) and Model Interpretability

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9 questions

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6 questions · about 4 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does local interpretability mean?
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Think about what it means to understand a specific decision.
Question 2.
Name one tool used for local interpretability.
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These tools make AI models more understandable.
3Question 3.
What does local interpretability aim to achieve?
- Understanding overall model performance
- Explaining individual predictions
- Improving model accuracy
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Think about what 'local' means in this context.
4Question 4.
True or False: SHAP provides a way to assign contributions to model predictions based on game theory.
- True
- False
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Consider what SHAP stands for.
Question 5.
Given a healthcare AI model predicting patient diagnoses, design a workflow using LIME and SHAP to explain a specific prediction to a doctor. Discuss the benefits of each tool in this scenario.
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Think about how you'd communicate the AI's decision to someone without a technical background.
Question 6.
Discuss the broader implications of local interpretability in AI for societal trust. Illustrate your answer with examples from finance or healthcare.
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Consider what could happen if people do not trust AI’s role in sensitive areas.

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