Practice Disparate Impact Analysis - 1.2.1 | Module 7: Advanced ML Topics & Ethical Considerations (Weeks 14) | Machine Learning
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1.2.1 - Disparate Impact Analysis

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does Disparate Impact Analysis seek to examine?

πŸ’‘ Hint: Think about outcomes related to demographic attributes.

Question 2

Easy

Define demographic parity in the context of machine learning.

πŸ’‘ Hint: Consider what fairness looks like.

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 Disparate Impact Analysis focus on?

  • Bias in AI outcomes
  • Data processing speed
  • User satisfaction

πŸ’‘ 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

πŸ’‘ Hint: Consider how fairness relates to decisions for all groups.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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.

πŸ’‘ Hint: Think about how different metrics can uncover hidden biases in AI.

Question 2

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

πŸ’‘ Hint: Consider how ethical decision-making interacts with health outcomes.

Challenge and get performance evaluation