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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is bias in AI?
💡 Hint: Think about data representation.
Question 2
Easy
Why is transparency important?
💡 Hint: Focus on understanding and acceptance.
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 is important to avoid in AI datasets?
- Homogeneity
- Diversity
- Consistency
💡 Hint: Think about how data represents different groups.
Question 2
True or False: Transparency in AI systems is optional.
- True
- False
💡 Hint: Remember the role of users trusting the system.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Analyze a situation where a biased AI system led to adverse consequences in hazard prediction. What measures could have been taken to prevent this?
💡 Hint: Focus on past examples and key measures like data diversity.
Question 2
Propose a framework for implementing accountability in AI hazard prediction systems. What challenges might arise?
💡 Hint: Consider the complexity of current systems.
Challenge and get performance evaluation