AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 250 · 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: leaves from every season of the year? (a) one-sided data (b) balanced data
2. 2.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
3. 3.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
4. 4.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
5. 5.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its colour (c) its training data (d) the weather
6. 6.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
7. 7.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
8. 8.True or false: “test on many groups” adds to bias. (a) True (b) False
9. 9.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
10. 10.🔍 Which is the odd one out? (a) add varied examples (b) test on one group only (c) use one group only (d) copy old unfair choices
11. 11.True or false: “use one group only” helps reduce bias. (a) True (b) False
12. 12.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
13. 13.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) bias from data (c) a broken screen (d) a strong battery
14. 14.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
15. 15.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
16. 16.🔍 Which is the odd one out? (a) check who is missing (b) add varied examples (c) add more of the same (d) test on many groups
17. 17.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
18. 18.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
19. 19.Which of these helps reduce bias? (a) ignore complaints (b) use one group only (c) test on many groups (d) add more of the same
20. 20.🔍 Which is the odd one out? (a) skip testing (b) add more of the same (c) fix wrong labels (d) copy old unfair choices
21. 21.Which of these adds to bias? (a) balance the data (b) add varied examples (c) copy old unfair choices (d) fix wrong labels
22. 22.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
23. 23.🔍 Which is the odd one out? (a) test on many groups (b) copy old unfair choices (c) ask different people (d) add varied examples
24. 24.Which of these adds to bias? (a) ask different people (b) add more of the same (c) fix wrong labels (d) check who is missing
25. 25.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
26. 26.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
27. 27.🔍 Which is the odd one out? (a) ask different people (b) use one group only (c) test on one group only (d) ignore complaints
28. 28.True or false: “test on one group only” helps reduce bias. (a) False (b) True
29. 29.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
30. 30.Which of these helps reduce bias? (a) ignore complaints (b) use one group only (c) check who is missing (d) skip testing
31. 31.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
32. 32.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
33. 33.Which of these adds to bias? (a) ask different people (b) skip testing (c) fix wrong labels (d) balance the data
34. 34.True or false: “test on many groups” helps reduce bias. (a) False (b) True
35. 35.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
36. 36.True or false: “add more of the same” helps reduce bias. (a) False (b) True
37. 37.🔍 Which is the odd one out? (a) add more of the same (b) use one group only (c) test on many groups (d) ignore complaints
38. 38.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
39. 39.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
40. 40.Which of these helps reduce bias? (a) test on one group only (b) add varied examples (c) add more of the same (d) skip testing

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