AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 828 · 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.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
2. 2.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
3. 3.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
4. 4.Which of these adds to bias? (a) ask different people (b) fix wrong labels (c) test on one group only (d) check who is missing
5. 5.Which of these helps reduce bias? (a) ignore complaints (b) use one group only (c) copy old unfair choices (d) ask different people
6. 6.True or false: “add varied examples” helps reduce bias. (a) True (b) False
7. 7.🔍 Which is the odd one out? (a) balance the data (b) test on many groups (c) test on one group only (d) fix wrong labels
8. 8.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
9. 9.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
10. 10.True or false: “check who is missing” helps reduce bias. (a) True (b) False
11. 11.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) fewer tests (c) more varied data (d) one group only
12. 12.Which of these helps reduce bias? (a) ignore complaints (b) add more of the same (c) add varied examples (d) skip testing
13. 13.🔍 Which is the odd one out? (a) use one group only (b) ignore complaints (c) ask different people (d) copy old unfair choices
14. 14.🔍 Which is the odd one out? (a) copy old unfair choices (b) add varied examples (c) check who is missing (d) balance the data
15. 15.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
16. 16.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
17. 17.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) children speak too fast (c) it was a rainy day (d) few child voices in data
18. 18.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
19. 19.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
20. 20.Which of these helps reduce bias? (a) balance the data (b) add more of the same (c) use one group only (d) ignore complaints
21. 21.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
22. 22.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
23. 23.🔍 Which is the odd one out? (a) add more of the same (b) copy old unfair choices (c) test on one group only (d) add varied examples
24. 24.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
25. 25.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
26. 26.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
27. 27.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
28. 28.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
29. 29.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its colour (c) the weather (d) its screen size
30. 30.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
31. 31.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
32. 32.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) False (b) True
33. 33.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
34. 34.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
35. 35.🔍 Which is the odd one out? (a) ignore complaints (b) balance the data (c) fix wrong labels (d) add varied examples
36. 36.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
37. 37.🔍 Which is the odd one out? (a) copy old unfair choices (b) test on one group only (c) skip testing (d) fix wrong labels
38. 38.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
39. 39.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
40. 40.🔍 Which is the odd one out? (a) skip testing (b) test on one group only (c) copy old unfair choices (d) balance the data

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