AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 73 · 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.Which of these helps reduce bias? (a) test on one group only (b) balance the data (c) add more of the same (d) skip testing
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: skip testing? (a) reduces bias (b) adds to bias
4. 4.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
5. 5.🔍 Which is the odd one out? (a) copy old unfair choices (b) add more of the same (c) fix wrong labels (d) skip testing
6. 6.🔍 Which is the odd one out? (a) fix wrong labels (b) add more of the same (c) test on many groups (d) add varied examples
7. 7.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) children speak too fast (c) few child voices in data (d) it was a rainy day
8. 8.🔍 Which is the odd one out? (a) check who is missing (b) test on one group only (c) ask different people (d) balance the data
9. 9.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
10. 10.🔍 Which is the odd one out? (a) balance the data (b) skip testing (c) fix wrong labels (d) test on many groups
11. 11.Which of these helps reduce bias? (a) check who is missing (b) skip testing (c) ignore complaints (d) add more of the same
12. 12.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a fair model (c) a broken screen (d) a strong battery
13. 13.True or false: “test on many groups” helps reduce bias. (a) False (b) True
14. 14.🔍 Which is the odd one out? (a) fix wrong labels (b) check who is missing (c) use one group only (d) test on many groups
15. 15.Which of these helps reduce bias? (a) use one group only (b) add varied examples (c) copy old unfair choices (d) add more of the same
16. 16.Which of these adds to bias? (a) add varied examples (b) check who is missing (c) fix wrong labels (d) ignore complaints
17. 17.True or false: “add varied examples” adds to bias. (a) False (b) True
18. 18.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
19. 19.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
20. 20.True or false: “ask different people” adds to bias. (a) True (b) False
21. 21.🔍 Which is the odd one out? (a) add more of the same (b) balance the data (c) skip testing (d) use one group only
22. 22.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) its training data (d) the weather
23. 23.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
24. 24.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
25. 25.Which of these helps reduce bias? (a) fix wrong labels (b) test on one group only (c) add more of the same (d) use one group only
26. 26.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
27. 27.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
28. 28.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
29. 29.Which of these adds to bias? (a) use one group only (b) balance the data (c) add varied examples (d) check who is missing
30. 30.Which of these helps reduce bias? (a) ignore complaints (b) test on one group only (c) skip testing (d) test on many groups
31. 31.🔍 Which is the odd one out? (a) ask different people (b) test on many groups (c) fix wrong labels (d) ignore complaints
32. 32.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
33. 33.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
34. 34.Which of these adds to bias? (a) ask different people (b) check who is missing (c) add varied examples (d) test on one group only
35. 35.True or false: “add more of the same” helps reduce bias. (a) False (b) True
36. 36.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
37. 37.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
38. 38.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
39. 39.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
40. 40.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True

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