AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 546 · 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 is the odd one out? (a) skip testing (b) check who is missing (c) test on one group only (d) add more of the same
2. 2.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) children speak too fast (c) it was a rainy day (d) the phone was new
3. 3.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
4. 4.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) False (b) True
5. 5.Which of these helps reduce bias? (a) test on many groups (b) copy old unfair choices (c) add more of the same (d) ignore complaints
6. 6.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
7. 7.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) bias from data (c) a strong battery (d) a broken screen
8. 8.True or false: “ignore complaints” adds to bias. (a) False (b) True
9. 9.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) its training data (d) the weather
10. 10.True or false: “test on many groups” adds to bias. (a) False (b) True
11. 11.Which of these adds to bias? (a) add varied examples (b) ask different people (c) fix wrong labels (d) test on one group only
12. 12.Which of these helps reduce bias? (a) ignore complaints (b) use one group only (c) fix wrong labels (d) skip testing
13. 13.🔍 Which is the odd one out? (a) copy old unfair choices (b) ignore complaints (c) use one group only (d) add varied examples
14. 14.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
15. 15.True or false: “balance the data” helps reduce bias. (a) True (b) False
16. 16.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
17. 17.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
18. 18.🔍 Which is the odd one out? (a) test on one group only (b) balance the data (c) fix wrong labels (d) check who is missing
19. 19.True or false: “check who is missing” helps reduce bias. (a) True (b) False
20. 20.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
21. 21.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
22. 22.Which of these helps reduce bias? (a) add more of the same (b) ask different people (c) ignore complaints (d) use one group only
23. 23.🔍 Which is the odd one out? (a) add varied examples (b) fix wrong labels (c) add more of the same (d) balance the data
24. 24.True or false: “add more of the same” adds to bias. (a) False (b) True
25. 25.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) more varied data (c) fewer tests (d) one group only
26. 26.Which of these adds to bias? (a) fix wrong labels (b) check who is missing (c) use one group only (d) add varied examples
27. 27.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) False (b) True
28. 28.🔍 Which is the odd one out? (a) skip testing (b) test on one group only (c) use one group only (d) test on many groups
29. 29.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
30. 30.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
31. 31.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
32. 32.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
33. 33.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
34. 34.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
35. 35.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
36. 36.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
37. 37.Which of these adds to bias? (a) test on many groups (b) add varied examples (c) ignore complaints (d) fix wrong labels
38. 38.🔍 Which is the odd one out? (a) test on one group only (b) copy old unfair choices (c) balance the data (d) skip testing
39. 39.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
40. 40.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

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