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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is bias in the context of AI?
💡 Hint: Think about how training data may be lopsided.
Question 2
Easy
What is meant by fairness-aware metrics?
💡 Hint: Consider metrics that ensure everyone is treated equally.
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 bias in AI usually stem from?
- Training data
- Algorithm design
- External environments
💡 Hint: Consider where the AI learns its information.
Question 2
True or False: All AI models are fair with no bias.
- True
- False
💡 Hint: Think about how data reflects reality.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Imagine you are tasked with creating a fairness-aware metric for an AI model that predicts loan approvals. Design this metric and justify your choices.
💡 Hint: Consider what characteristics will indicate equitable treatment across groups.
Question 2
Reflect on a popular case where AI bias has negatively impacted a community. Discuss how you might redesign the training data to rectify such bias.
💡 Hint: Think about the variety of people included in training samples.
Challenge and get performance evaluation