Practice - Evaluation Bias (Performance Measurement Bias)
Practice Questions
Test your understanding with targeted questions
Define evaluation bias in your own words.
💡 Hint: Think about how metrics can mislead if not detailed.
What is demographic parity?
💡 Hint: Remember, it’s about outcome equality.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What does evaluation bias refer to?
💡 Hint: Consider what happens when metrics do not reflect true performance.
True or False: High overall accuracy guarantees that an AI system is fair.
💡 Hint: Think about what accuracy measures; is it always enough?
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Challenge Problems
Push your limits with advanced challenges
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.
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.
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Reference links
Supplementary resources to enhance your learning experience.