AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 990 · 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.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
2. 2.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
3. 3.🔍 Which is the odd one out? (a) ask different people (b) add varied examples (c) check who is missing (d) add more of the same
4. 4.Which of these helps reduce bias? (a) ignore complaints (b) use one group only (c) fix wrong labels (d) copy old unfair choices
5. 5.🛠️ What helps make an AI system fairer? (a) one group only (b) more varied data (c) fewer tests (d) ignoring mistakes
6. 6.🔍 Which is the odd one out? (a) check who is missing (b) ask different people (c) use one group only (d) fix wrong labels
7. 7.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
8. 8.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) it was a rainy day (c) the phone was new (d) few child voices in data
9. 9.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
10. 10.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
11. 11.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) the weather (c) its training data (d) its colour
12. 12.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
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.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
15. 15.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a strong battery (c) a fair model (d) a broken screen
16. 16.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
17. 17.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
18. 18.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
19. 19.🔍 Which is the odd one out? (a) test on many groups (b) check who is missing (c) balance the data (d) skip testing
20. 20.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
21. 21.Which of these adds to bias? (a) check who is missing (b) ignore complaints (c) add varied examples (d) ask different people
22. 22.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
23. 23.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
24. 24.Which of these adds to bias? (a) add varied examples (b) balance the data (c) test on one group only (d) fix wrong labels
25. 25.🔍 Which is the odd one out? (a) copy old unfair choices (b) skip testing (c) ask different people (d) ignore complaints
26. 26.Which of these helps reduce bias? (a) skip testing (b) use one group only (c) add more of the same (d) check who is missing
27. 27.Which of these adds to bias? (a) ask different people (b) check who is missing (c) use one group only (d) balance the data
28. 28.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
29. 29.True or false: “check who is missing” adds to bias. (a) False (b) True
30. 30.True or false: “fix wrong labels” adds to bias. (a) False (b) True
31. 31.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
32. 32.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
33. 33.True or false: “add more of the same” helps reduce bias. (a) False (b) True
34. 34.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
35. 35.True or false: “ask different people” helps reduce bias. (a) False (b) True
36. 36.🔍 Which is the odd one out? (a) copy old unfair choices (b) test on one group only (c) add more of the same (d) check who is missing
37. 37.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
38. 38.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
39. 39.True or false: “test on one group only” helps reduce bias. (a) True (b) False
40. 40.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

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