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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does Disparate Impact Analysis seek to examine?
π‘ Hint: Think about outcomes related to demographic attributes.
Question 2
Easy
Define demographic parity in the context of machine learning.
π‘ Hint: Consider what fairness looks like.
Practice 4 more questions and get performance evaluation
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What does Disparate Impact Analysis focus on?
π‘ 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.
π‘ Hint: Consider how fairness relates to decisions for all groups.
Solve 1 more question and get performance evaluation
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.
π‘ 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.
π‘ Hint: Consider how ethical decision-making interacts with health outcomes.
Challenge and get performance evaluation