AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 725 · 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: “ignore complaints” helps reduce bias. (a) True (b) False
2. 2.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
3. 3.🗣️ 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
4. 4.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) bias from data (c) a broken screen (d) a fair model
5. 5.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
6. 6.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
7. 7.🔍 Which is the odd one out? (a) test on one group only (b) use one group only (c) ask different people (d) add more of the same
8. 8.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
9. 9.🔍 Which is the odd one out? (a) add varied examples (b) test on one group only (c) fix wrong labels (d) check who is missing
10. 10.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
11. 11.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
12. 12.True or false: “use one group only” helps reduce bias. (a) True (b) False
13. 13.🔍 Which is the odd one out? (a) add more of the same (b) copy old unfair choices (c) test on many groups (d) use one group only
14. 14.Which of these helps reduce bias? (a) add varied examples (b) skip testing (c) ignore complaints (d) copy old unfair choices
15. 15.True or false: “add more of the same” adds to bias. (a) True (b) False
16. 16.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
17. 17.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
18. 18.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
19. 19.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
20. 20.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
21. 21.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
22. 22.True or false: “use one group only” adds to bias. (a) True (b) False
23. 23.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) its training data (d) the weather
24. 24.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
25. 25.Which of these helps reduce bias? (a) use one group only (b) ask different people (c) add more of the same (d) copy old unfair choices
26. 26.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) skip testing (d) balance the data
27. 27.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
28. 28.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
29. 29.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
30. 30.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
31. 31.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
32. 32.🛠️ What helps make an AI system fairer? (a) one group only (b) more varied data (c) fewer tests (d) ignoring mistakes
33. 33.Which of these adds to bias? (a) test on many groups (b) balance the data (c) add varied examples (d) test on one group only
34. 34.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
35. 35.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
36. 36.Which of these adds to bias? (a) test on many groups (b) add more of the same (c) add varied examples (d) fix wrong labels
37. 37.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
38. 38.🔍 Which is the odd one out? (a) skip testing (b) fix wrong labels (c) use one group only (d) copy old unfair choices
39. 39.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
40. 40.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data

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