AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 740 · 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.Which of these adds to bias? (a) skip testing (b) balance the data (c) ask different people (d) fix wrong labels
2. 2.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
3. 3.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
4. 4.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
5. 5.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
6. 6.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
7. 7.🔍 Which is the odd one out? (a) add more of the same (b) check who is missing (c) ask different people (d) balance the data
8. 8.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
9. 9.True or false: “test on one group only” helps reduce bias. (a) True (b) False
10. 10.Which of these helps reduce bias? (a) test on one group only (b) use one group only (c) ask different people (d) add more of the same
11. 11.Which of these adds to bias? (a) test on many groups (b) check who is missing (c) use one group only (d) balance the data
12. 12.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) few child voices in data (c) the phone was new (d) children speak too fast
13. 13.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a broken screen (c) a strong battery (d) bias from data
14. 14.Which of these helps reduce bias? (a) test on one group only (b) balance the data (c) ignore complaints (d) use one group only
15. 15.Which of these adds to bias? (a) copy old unfair choices (b) check who is missing (c) add varied examples (d) fix wrong labels
16. 16.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
17. 17.🔍 Which is the odd one out? (a) ignore complaints (b) skip testing (c) balance the data (d) use one group only
18. 18.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
19. 19.True or false: “add more of the same” adds to bias. (a) False (b) True
20. 20.True or false: “balance the data” adds to bias. (a) True (b) False
21. 21.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
22. 22.🔍 Which is the odd one out? (a) check who is missing (b) copy old unfair choices (c) test on one group only (d) add more of the same
23. 23.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
24. 24.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its training data (d) its screen size
25. 25.🔍 Which is the odd one out? (a) skip testing (b) copy old unfair choices (c) ignore complaints (d) ask different people
26. 26.True or false: “test on many groups” adds to bias. (a) True (b) False
27. 27.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) more varied data (c) one group only (d) fewer tests
28. 28.True or false: “add varied examples” adds to bias. (a) True (b) False
29. 29.Which of these adds to bias? (a) test on many groups (b) check who is missing (c) ignore complaints (d) balance the data
30. 30.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
31. 31.True or false: “check who is missing” helps reduce bias. (a) True (b) False
32. 32.True or false: “test on many groups” helps reduce bias. (a) False (b) True
33. 33.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
34. 34.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
35. 35.🔍 Which is the odd one out? (a) ignore complaints (b) skip testing (c) add varied examples (d) use one group only
36. 36.🔍 Which is the odd one out? (a) check who is missing (b) ask different people (c) use one group only (d) add varied examples
37. 37.🔍 Which is the odd one out? (a) add more of the same (b) test on one group only (c) test on many groups (d) copy old unfair choices
38. 38.🔍 Which is the odd one out? (a) check who is missing (b) fix wrong labels (c) test on one group only (d) test on many groups
39. 39.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
40. 40.Which of these adds to bias? (a) fix wrong labels (b) add varied examples (c) test on one group only (d) test on many groups

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