AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 462 · 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: voices of children and adults from many regions? (a) balanced data (b) one-sided data
2. 2.Which of these adds to bias? (a) test on one group only (b) ask different people (c) add varied examples (d) test on many groups
3. 3.Which of these helps reduce bias? (a) copy old unfair choices (b) add more of the same (c) ask different people (d) use one group only
4. 4.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
5. 5.Which of these helps reduce bias? (a) test on one group only (b) use one group only (c) ignore complaints (d) balance the data
6. 6.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
7. 7.True or false: “ignore complaints” adds to bias. (a) True (b) False
8. 8.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
9. 9.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
10. 10.True or false: “fix wrong labels” adds to bias. (a) True (b) False
11. 11.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
12. 12.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
13. 13.🔍 Which is the odd one out? (a) use one group only (b) ask different people (c) skip testing (d) ignore complaints
14. 14.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
15. 15.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
16. 16.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) its training data (d) the weather
17. 17.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
18. 18.🔍 Which is the odd one out? (a) test on many groups (b) check who is missing (c) test on one group only (d) ask different people
19. 19.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
20. 20.True or false: “balance the data” adds to bias. (a) False (b) True
21. 21.Which of these adds to bias? (a) copy old unfair choices (b) fix wrong labels (c) balance the data (d) add varied examples
22. 22.🛠️ What helps make an AI system fairer? (a) more varied data (b) ignoring mistakes (c) fewer tests (d) one group only
23. 23.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
24. 24.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
25. 25.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) True (b) False
26. 26.🖼️ 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
27. 27.🗣️ 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
28. 28.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
29. 29.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
30. 30.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
31. 31.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
32. 32.Which of these helps reduce bias? (a) add varied examples (b) skip testing (c) use one group only (d) ignore complaints
33. 33.Which of these adds to bias? (a) ask different people (b) ignore complaints (c) balance the data (d) test on many groups
34. 34.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
35. 35.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
36. 36.🔍 Which is the odd one out? (a) skip testing (b) add more of the same (c) copy old unfair choices (d) fix wrong labels
37. 37.True or false: “ask different people” adds to bias. (a) True (b) False
38. 38.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
39. 39.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
40. 40.True or false: “test on one group only” adds to bias. (a) True (b) False

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