Practice Labeling Bias (2.3) - AI Ethics, Bias, and Responsible AI

Practise this section

9 questions

Flashcard drill
2 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
Sign up to start the exercise
Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
Sign up to start the quiz

6 sample questions

Question 1.
Define labeling bias.
Show hintHide hint
Think about how human perspectives can differ.
Question 2.
What is annotation in the context of AI?
Show hintHide hint
Consider what it means to provide a label or category.
3Question 3.
What is labeling bias?
- A systematic error in data
- Subjective labeling influenced by personal biases
- Improper data collection
Show hintHide hint
Focus on the subjective aspect of labeling.
4Question 4.
True or False: Labeling bias can lead to fair AI outcomes.
- True
- False
Show hintHide hint
Think about the nature of bias and fairness.
Question 5.
Design a data annotation process that minimizes labeling bias while ensuring diversity. What steps would you include?
Show hintHide hint
Consider aspects of diversity and fairness in your design.
Question 6.
Critically evaluate a past incident where labeling bias led to significant negative outcomes, such as social media moderation errors. Propose a solution for the future.
Show hintHide hint
Reflect on recent news regarding AI failures to inform your evaluation.

5 more questions in this section. Enroll free to practise them all.