Practice Counterfactual Explanations (3.4) - Explainable AI (XAI) and Model Interpretability

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6 sample questions

Question 1.
What are counterfactual explanations?
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Think of the 'what if' scenarios.
Question 2.
Why are counterfactual explanations important?
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Consider their role in understanding decisions.
3Question 3.
What is the main purpose of counterfactual explanations?
- To change model architecture
- To analyze changes in outcomes
- To enhance computational speed
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Focus on the 'what if' aspect.
4Question 4.
True or False: Counterfactuals can help in identifying bias in AI models.
- True
- False
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Reflect on fairness and accuracy.
Question 5.
Design a counterfactual scenario for a hiring algorithm to understand how various qualifications affect hiring decisions.
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Focus on individual qualifications adjustments.
Question 6.
Critique the role of counterfactual explanations in addressing bias in AI systems. Provide examples.
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Consider real-world applications such as credit scoring.

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