Practice Fairness Metrics (Quantitative Assessment) - 1.2.2 | Module 7: Advanced ML Topics & Ethical Considerations (Weeks 14) | Machine Learning
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1.2.2 - Fairness Metrics (Quantitative Assessment)

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is Demographic Parity?

πŸ’‘ Hint: Think about equal treatment in terms of outcomes.

Question 2

Easy

Define Equal Opportunity.

πŸ’‘ Hint: Focus on those who qualify for a positive outcome.

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 Demographic Parity assess?

  • Equal outcomes across groups
  • Accuracy across groups
  • True positive rates

πŸ’‘ Hint: Focus on outcome equality.

Question 2

True or False: Equal Opportunity focuses on similar true positive rates among all groups.

  • True
  • False

πŸ’‘ Hint: Think of qualifications.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Evaluate the implications of not assessing fairness metrics in a predictive policing algorithm. How could this impact community trust?

πŸ’‘ Hint: Think about the relationship between law enforcement and community perception.

Question 2

Create a strategy for integrating fairness metrics into a job recruitment AI system to mitigate biases.

πŸ’‘ Hint: Consider how to engage various groups in the process.

Challenge and get performance evaluation