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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
Define bias in the context of machine learning.
💡 Hint: Think about how historical data can influence AI decisions.
Question 2
Easy
What is accountability in AI?
💡 Hint: Consider who is responsible when an AI system makes a mistake.
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 bias in AI?
- An improvement in model performance
- A systematic prejudice leading to unfair outcomes
- A measure of accuracy
💡 Hint: Consider how data can reflect societal inequalities.
Question 2
True or False: Transparency in AI helps foster public trust.
- True
- False
💡 Hint: Think about how clear information affects opinions.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Design an AI system intended for credit scoring. Discuss the potential biases that could arise, how you would detect them, and what strategies you’d implement to mitigate them.
💡 Hint: Consider both the data sources and the algorithm choices.
Question 2
Given a scenario where an AI model exhibited unfair loan denial rates for a demographic group, detail an ethical analysis framework based on the ethical principles covered in this section.
💡 Hint: Reflect on the principles discussed earlier in our sessions.
Challenge and get performance evaluation