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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is a potential consequence of using biased training data in AI?
💡 Hint: Think about fairness.
Question 2
Easy
Name one way to prevent bias during model training.
💡 Hint: Consider how diversity affects results.
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 one way to ensure fairness in AI model training?
- Use biased data
- Incorporate diverse datasets
- Ignore ethical principles
💡 Hint: Consider what diversity implies.
Question 2
True or False: Accountability in AI model training means developers are responsible for the AI's performance.
- True
- False
💡 Hint: Think about responsibilities.
Solve and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Design a training model to mitigate bias in AI hiring tools. What specific methods would you implement?
💡 Hint: Reflect on previous discussions around ethical practices.
Question 2
Analyze the implications of failing to test AI models for bias in real-world applications. Provide examples.
💡 Hint: Think about the social impact of technology.
Challenge and get performance evaluation