AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 404 · 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) balance the data (b) check who is missing (c) test on many groups (d) skip testing
2. 2.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
3. 3.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a strong battery (c) a broken screen (d) bias from data
4. 4.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
5. 5.True or false: “add varied examples” helps reduce bias. (a) False (b) True
6. 6.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
7. 7.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
8. 8.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
9. 9.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) ignore complaints (d) ask different people
10. 10.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) more varied data (d) ignoring mistakes
11. 11.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
12. 12.True or false: “check who is missing” adds to bias. (a) False (b) True
13. 13.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
14. 14.🔍 Which is the odd one out? (a) ask different people (b) add more of the same (c) balance the data (d) fix wrong labels
15. 15.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
16. 16.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
17. 17.🔍 Which is the odd one out? (a) use one group only (b) copy old unfair choices (c) ask different people (d) skip testing
18. 18.Which of these adds to bias? (a) add more of the same (b) test on many groups (c) balance the data (d) fix wrong labels
19. 19.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
20. 20.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
21. 21.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) the phone was new (c) children speak too fast (d) it was a rainy day
22. 22.Which of these helps reduce bias? (a) balance the data (b) copy old unfair choices (c) use one group only (d) test on one group only
23. 23.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
24. 24.🔍 Which is the odd one out? (a) test on many groups (b) use one group only (c) add more of the same (d) copy old unfair choices
25. 25.🔍 Which is the odd one out? (a) fix wrong labels (b) balance the data (c) copy old unfair choices (d) ask different people
26. 26.True or false: “fix wrong labels” adds to bias. (a) True (b) False
27. 27.Which of these adds to bias? (a) copy old unfair choices (b) check who is missing (c) ask different people (d) fix wrong labels
28. 28.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False
29. 29.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) the weather (c) its colour (d) its screen size
30. 30.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) ignore complaints (d) ask different people
31. 31.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
32. 32.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
33. 33.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
34. 34.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
35. 35.True or false: “check who is missing” helps reduce bias. (a) False (b) True
36. 36.🔍 Which is the odd one out? (a) check who is missing (b) test on one group only (c) add more of the same (d) use one group only
37. 37.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
38. 38.🔍 Which is the odd one out? (a) use one group only (b) copy old unfair choices (c) fix wrong labels (d) add more of the same
39. 39.Which of these adds to bias? (a) ask different people (b) balance the data (c) add varied examples (d) skip testing
40. 40.🔍 Which is the odd one out? (a) test on one group only (b) ignore complaints (c) copy old unfair choices (d) add varied examples

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