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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is meant by 'anonymization' in data collection?
💡 Hint: Think about how to protect user identities.
Question 2
Easy
Why is fairness important in model training?
💡 Hint: Consider the impacts of biased data on decision making.
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 the key ethical focus during data collection?
- Anonymization
- Speed
- Cost
💡 Hint: Consider why privacy is important.
Question 2
True or False: Bias is acceptable in AI if it results in better performance.
- True
- False
💡 Hint: Think about fairness in decision-making.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Design a process to ethically collect data from a group that contains sensitive demographic information. Discuss how you would ensure consent and fairness.
💡 Hint: Think about ethics and inclusion.
Question 2
Propose a framework for monitoring AI systems to prevent bias. What metrics would you include and how would you analyze them?
💡 Hint: Consider ongoing evaluations and user input.
Challenge and get performance evaluation