AI Bias Worksheet for Kids

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

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