### Interactive Quiz: AI Fairness
Welcome to our interactive quiz on AI Fairness. This quiz is designed to test your understanding of the key concepts, challenges, and best practices related to fairness in artificial intelligence.
#### Question 1:
**What is the primary goal of ensuring fairness in AI?**
A) To increase the accuracy of AI models
B) To make AI systems more efficient
C) To prevent bias and discrimination in AI outcomes
D) To reduce the cost of AI development
Please select the correct answer by typing the letter corresponding to your choice.
#### Question 2:
**Which of the following is NOT a common type of bias in AI?**
A) Selection bias
B) Confirmation bias
C) Prejudicial bias
D) Sampling bias
Please select the correct answer by typing the letter corresponding to your choice.
#### Question 3:
**True or False: Fairness in AI is only relevant for decision-making systems that impact human lives.**
A) True
B) False
Please type ‘True’ or ‘False’ to indicate your answer.
#### Question 4:
**What is one common method used to mitigate bias in AI models?**
A) Increasing the amount of training data
B) Removing features that are correlated with sensitive attributes
C) Ignoring the problem of bias altogether
D) Using only open-source datasets
Please select the correct answer by typing the letter corresponding to your choice.
#### Question 5:
**Which of the following is a well-known fairness metric used to evaluate AI models?**
A) Precision
B) Recall
C) Fairness through Awareness (FTA)
D) Confusion Matrix
Please select the correct answer by typing the letter corresponding to your choice.
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### Answers:
#### Question 1:
**Correct Answer: C) To prevent bias and discrimination in AI outcomes**
#### Question 2:
**Correct Answer: C) Prejudicial bias**
#### Question 3:
**Correct Answer: B) False**
#### Question 4:
**Correct Answer: B) Removing features that are correlated with sensitive attributes**
#### Question 5:
**Correct Answer: C) Fairness through Awareness (FTA)**
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Thank you for participating in our quiz on AI Fairness. Your understanding and commitment to ensuring fairness in AI are crucial for building more equitable and unbiased systems. If you have any questions or need further clarification on any of the topics covered, please don’t hesitate to ask. We are here to help!