Practice Disparate Impact Analysis (1.2.1) - Advanced ML Topics & Ethical Considerations (Weeks 14)
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Disparate Impact Analysis

Practice - Disparate Impact Analysis

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does Disparate Impact Analysis seek to examine?

💡 Hint: Think about outcomes related to demographic attributes.

Question 2 Easy

Define demographic parity in the context of machine learning.

💡 Hint: Consider what fairness looks like.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does Disparate Impact Analysis focus on?

Bias in AI outcomes
Data processing speed
User satisfaction

💡 Hint: Remember what we learned about fairness in AI.

Question 2

True or False: A higher false positive rate is acceptable if the overall accuracy is high.

True
False

💡 Hint: Consider how fairness relates to decisions for all groups.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

An AI model for loan approval shows 95% accuracy overall, but 70% for women applicants. Discuss the implications and what fairness measures should be put in place.

💡 Hint: Think about how different metrics can uncover hidden biases in AI.

Challenge 2 Hard

Analyze how a Disparate Impact Analysis can influence a hospital's AI-driven patient assignment system.

💡 Hint: Consider how ethical decision-making interacts with health outcomes.

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