AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 726 · 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 is the odd one out? (a) test on many groups (b) use one group only (c) check who is missing (d) ask different people
2. 2.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
3. 3.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a fair model (c) bias from data (d) a strong battery
4. 4.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
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.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) children speak too fast (c) few child voices in data (d) the phone was new
7. 7.True or false: “test on one group only” helps reduce bias. (a) False (b) True
8. 8.Which of these helps reduce bias? (a) use one group only (b) copy old unfair choices (c) check who is missing (d) ignore complaints
9. 9.True or false: “test on many groups” helps reduce bias. (a) True (b) False
10. 10.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
11. 11.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
12. 12.True or false: “ask different people” helps reduce bias. (a) True (b) False
13. 13.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
14. 14.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
15. 15.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
16. 16.True or false: “ask different people” adds to bias. (a) False (b) True
17. 17.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
18. 18.🔍 Which is the odd one out? (a) test on many groups (b) use one group only (c) add more of the same (d) copy old unfair choices
19. 19.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
20. 20.Which of these helps reduce bias? (a) use one group only (b) test on one group only (c) fix wrong labels (d) copy old unfair choices
21. 21.Which of these adds to bias? (a) check who is missing (b) ask different people (c) ignore complaints (d) add varied examples
22. 22.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
23. 23.True or false: “fix wrong labels” adds to bias. (a) True (b) False
24. 24.True or false: “use one group only” adds to bias. (a) True (b) False
25. 25.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
26. 26.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
27. 27.🔍 Which is the odd one out? (a) skip testing (b) check who is missing (c) balance the data (d) fix wrong labels
28. 28.🔍 Which is the odd one out? (a) copy old unfair choices (b) add more of the same (c) balance the data (d) use one group only
29. 29.True or false: “skip testing” adds to bias. (a) False (b) True
30. 30.Which of these adds to bias? (a) ask different people (b) add varied examples (c) test on many groups (d) add more of the same
31. 31.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
32. 32.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
33. 33.🔍 Which is the odd one out? (a) check who is missing (b) test on one group only (c) ignore complaints (d) skip testing
34. 34.🔍 Which is the odd one out? (a) ask different people (b) test on many groups (c) copy old unfair choices (d) add varied examples
35. 35.Which of these adds to bias? (a) add varied examples (b) ask different people (c) use one group only (d) balance the data
36. 36.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
37. 37.Which of these adds to bias? (a) balance the data (b) add varied examples (c) test on one group only (d) check who is missing
38. 38.True or false: “test on many groups” adds to bias. (a) False (b) True
39. 39.🔍 Which is the odd one out? (a) skip testing (b) add varied examples (c) ignore complaints (d) add more of the same
40. 40.Which of these adds to bias? (a) test on many groups (b) balance the data (c) skip testing (d) check who is missing

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