AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 81 · 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.True or false: “add varied examples” adds to bias. (a) True (b) False
2. 2.True or false: “balance the data” adds to bias. (a) True (b) False
3. 3.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
4. 4.🗣️ 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) it was a rainy day (d) children speak too fast
5. 5.🔍 Which is the odd one out? (a) add varied examples (b) ask different people (c) copy old unfair choices (d) test on many groups
6. 6.Which of these adds to bias? (a) ask different people (b) balance the data (c) test on many groups (d) add more of the same
7. 7.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
8. 8.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
9. 9.Which of these helps reduce bias? (a) add varied examples (b) add more of the same (c) use one group only (d) copy old unfair choices
10. 10.🔍 Which is the odd one out? (a) add more of the same (b) ignore complaints (c) test on one group only (d) add varied examples
11. 11.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) its training data (d) the weather
12. 12.🔍 Which is the odd one out? (a) test on many groups (b) balance the data (c) add more of the same (d) add varied examples
13. 13.🔍 Which is the odd one out? (a) use one group only (b) add more of the same (c) fix wrong labels (d) ignore complaints
14. 14.True or false: “fix wrong labels” adds to bias. (a) False (b) True
15. 15.Which of these helps reduce bias? (a) use one group only (b) add more of the same (c) check who is missing (d) copy old unfair choices
16. 16.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
17. 17.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a fair model (c) a strong battery (d) a broken screen
18. 18.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
19. 19.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
20. 20.True or false: “check who is missing” helps reduce bias. (a) True (b) False
21. 21.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
22. 22.True or false: “test on many groups” helps reduce bias. (a) True (b) False
23. 23.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
24. 24.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
25. 25.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
26. 26.🔍 Which is the odd one out? (a) balance the data (b) check who is missing (c) use one group only (d) add varied examples
27. 27.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
28. 28.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
29. 29.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
30. 30.True or false: “add more of the same” adds to bias. (a) True (b) False
31. 31.True or false: “test on one group only” adds to bias. (a) True (b) False
32. 32.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
33. 33.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
34. 34.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
35. 35.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
36. 36.Which of these helps reduce bias? (a) use one group only (b) copy old unfair choices (c) test on one group only (d) fix wrong labels
37. 37.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) True (b) False
38. 38.🔍 Which is the odd one out? (a) check who is missing (b) test on many groups (c) test on one group only (d) add varied examples
39. 39.Which of these helps reduce bias? (a) test on one group only (b) copy old unfair choices (c) add more of the same (d) balance the data
40. 40.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data

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