Practice Fairness Metrics (quantitative Assessment) (1.2.2) - Advanced ML Topics & Ethical Considerations (Weeks 14)
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Fairness Metrics (Quantitative Assessment)

Practice - Fairness Metrics (Quantitative Assessment)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is Demographic Parity?

💡 Hint: Think about equal treatment in terms of outcomes.

Question 2 Easy

Define Equal Opportunity.

💡 Hint: Focus on those who qualify for a positive outcome.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does Demographic Parity assess?

Equal outcomes across groups
Accuracy across groups
True positive rates

💡 Hint: Focus on outcome equality.

Question 2

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

True
False

💡 Hint: Think of qualifications.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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

💡 Hint: Think about the relationship between law enforcement and community perception.

Challenge 2 Hard

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

💡 Hint: Consider how to engage various groups in the process.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.