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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
Define evaluation bias in your own words.
💡 Hint: Think about how metrics can mislead if not detailed.
Question 2
Easy
What is demographic parity?
💡 Hint: Remember, it’s about outcome equality.
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 evaluation bias refer to?
- A bias related to data quality
- A bias in performance measurement
- A bias in algorithm design
💡 Hint: Consider what happens when metrics do not reflect true performance.
Question 2
True or False: High overall accuracy guarantees that an AI system is fair.
- True
- False
💡 Hint: Think about what accuracy measures; is it always enough?
Solve and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Explore a case study in which an AI model reflecting excellent overall performance failed to serve a particular demographic. Analyze the stages of evaluation bias leading to this issue.
💡 Hint: Begin with a comprehensive assessment of the model's metrics.
Question 2
A recent deployment of an ML algorithm shows high overall accuracy but disparities in outcomes for minority groups. How would you structure a systematic approach to uncover biases?
💡 Hint: Focus on both detecting and evaluating existing biases.
Challenge and get performance evaluation