AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 787 · 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.🔍 Which is the odd one out? (a) check who is missing (b) test on many groups (c) ignore complaints (d) add varied examples
3. 3.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
4. 4.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
5. 5.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
6. 6.Which of these helps reduce bias? (a) test on one group only (b) copy old unfair choices (c) add more of the same (d) balance the data
7. 7.🔍 Which is the odd one out? (a) test on one group only (b) skip testing (c) copy old unfair choices (d) add varied examples
8. 8.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
9. 9.🔍 Which is the odd one out? (a) check who is missing (b) add varied examples (c) copy old unfair choices (d) fix wrong labels
10. 10.🔍 Which is the odd one out? (a) fix wrong labels (b) add more of the same (c) check who is missing (d) ask different people
11. 11.Which of these adds to bias? (a) add more of the same (b) add varied examples (c) balance the data (d) check who is missing
12. 12.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) it was a rainy day (c) the phone was new (d) children speak too fast
13. 13.🔍 Which is the odd one out? (a) use one group only (b) copy old unfair choices (c) add more of the same (d) fix wrong labels
14. 14.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
15. 15.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
16. 16.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
17. 17.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
18. 18.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
19. 19.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
20. 20.True or false: “balance the data” adds to bias. (a) True (b) False
21. 21.True or false: “check who is missing” adds to bias. (a) True (b) False
22. 22.🔍 Which is the odd one out? (a) ignore complaints (b) copy old unfair choices (c) test on many groups (d) add more of the same
23. 23.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
24. 24.🔍 Which is the odd one out? (a) ask different people (b) use one group only (c) add more of the same (d) test on one group only
25. 25.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
26. 26.True or false: “add varied examples” helps reduce bias. (a) True (b) False
27. 27.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
28. 28.Which of these helps reduce bias? (a) add more of the same (b) copy old unfair choices (c) ignore complaints (d) ask different people
29. 29.True or false: “test on many groups” helps reduce bias. (a) False (b) True
30. 30.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) ask different people (d) ignore complaints
31. 31.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
32. 32.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
33. 33.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
34. 34.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
35. 35.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a strong battery (c) a broken screen (d) bias from data
36. 36.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
37. 37.Which of these helps reduce bias? (a) use one group only (b) add varied examples (c) skip testing (d) test on one group only
38. 38.True or false: “test on many groups” adds to bias. (a) True (b) False
39. 39.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
40. 40.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False

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