AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 805 · 40 questions

AI tip (Fair data, fairer AI): Bias usually comes from the training data: if a group is missing or shown in only one way, the model copies that. One-sided data: one age group, only ripe mangoes, only adult voices, only neat handwriting. Balanced data: many ages, colours, voices, regions and styles. Reduce bias: add varied examples, check who is missing, test on many groups, fix wrong labels and listen to complaints.

1. 1.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its screen size (c) the weather (d) its colour
2. 2.🔍 Which is the odd one out? (a) copy old unfair choices (b) test on one group only (c) balance the data (d) ignore complaints
3. 3.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
4. 4.🔍 Which is the odd one out? (a) test on many groups (b) check who is missing (c) add more of the same (d) balance the data
5. 5.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
6. 6.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
7. 7.🔍 Which is the odd one out? (a) test on many groups (b) check who is missing (c) fix wrong labels (d) use one group only
8. 8.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
9. 9.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
10. 10.🔍 Which is the odd one out? (a) ask different people (b) skip testing (c) add varied examples (d) fix wrong labels
11. 11.True or false: “use one group only” adds to bias. (a) True (b) False
12. 12.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
13. 13.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
14. 14.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
15. 15.Which of these helps reduce bias? (a) skip testing (b) test on one group only (c) copy old unfair choices (d) balance the data
16. 16.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
17. 17.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
18. 18.🔍 Which is the odd one out? (a) balance the data (b) copy old unfair choices (c) add varied examples (d) test on many groups
19. 19.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) more varied data (c) fewer tests (d) one group only
20. 20.🔍 Which is the odd one out? (a) use one group only (b) copy old unfair choices (c) ask different people (d) ignore complaints
21. 21.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
22. 22.True or false: “skip testing” adds to bias. (a) False (b) True
23. 23.True or false: “use one group only” helps reduce bias. (a) True (b) False
24. 24.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
25. 25.🔍 Which is the odd one out? (a) copy old unfair choices (b) test on one group only (c) add varied examples (d) use one group only
26. 26.Which of these helps reduce bias? (a) add more of the same (b) test on many groups (c) ignore complaints (d) test on one group only
27. 27.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) True (b) False
28. 28.Which of these helps reduce bias? (a) add varied examples (b) test on one group only (c) ignore complaints (d) skip testing
29. 29.🔍 Which is the odd one out? (a) fix wrong labels (b) check who is missing (c) add varied examples (d) test on one group only
30. 30.True or false: “check who is missing” helps reduce bias. (a) False (b) True
31. 31.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) False (b) True
32. 32.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
33. 33.Which of these adds to bias? (a) balance the data (b) test on many groups (c) copy old unfair choices (d) ask different people
34. 34.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
35. 35.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
36. 36.Which of these helps reduce bias? (a) ignore complaints (b) copy old unfair choices (c) skip testing (d) ask different people
37. 37.Which of these adds to bias? (a) balance the data (b) fix wrong labels (c) add more of the same (d) test on many groups
38. 38.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
39. 39.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
40. 40.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias

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