AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 552 · 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 one group only (b) fix wrong labels (c) add varied examples (d) test on many groups
2. 2.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
3. 3.Which of these adds to bias? (a) use one group only (b) ask different people (c) test on many groups (d) check who is missing
4. 4.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
5. 5.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
6. 6.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
7. 7.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
8. 8.🔍 Which is the odd one out? (a) use one group only (b) fix wrong labels (c) copy old unfair choices (d) test on one group only
9. 9.Which of these adds to bias? (a) check who is missing (b) test on one group only (c) test on many groups (d) fix wrong labels
10. 10.Which of these adds to bias? (a) check who is missing (b) add varied examples (c) test on many groups (d) skip testing
11. 11.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
12. 12.Which of these helps reduce bias? (a) add more of the same (b) skip testing (c) copy old unfair choices (d) check who is missing
13. 13.True or false: “use one group only” adds to bias. (a) False (b) True
14. 14.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
15. 15.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) the phone was new (c) few child voices in data (d) it was a rainy day
16. 16.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
17. 17.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
18. 18.Which of these adds to bias? (a) balance the data (b) check who is missing (c) fix wrong labels (d) ignore complaints
19. 19.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
20. 20.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
21. 21.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
22. 22.True or false: “test on many groups” helps reduce bias. (a) True (b) False
23. 23.Which of these adds to bias? (a) check who is missing (b) copy old unfair choices (c) fix wrong labels (d) ask different people
24. 24.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
25. 25.True or false: “ask different people” adds to bias. (a) False (b) True
26. 26.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
27. 27.Which of these helps reduce bias? (a) ask different people (b) use one group only (c) test on one group only (d) ignore complaints
28. 28.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
29. 29.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
30. 30.🔍 Which is the odd one out? (a) check who is missing (b) ask different people (c) ignore complaints (d) add varied examples
31. 31.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a broken screen (c) bias from data (d) a strong battery
32. 32.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
33. 33.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
34. 34.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
35. 35.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
36. 36.Which of these helps reduce bias? (a) copy old unfair choices (b) use one group only (c) balance the data (d) skip testing
37. 37.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
38. 38.🔍 Which is the odd one out? (a) balance the data (b) skip testing (c) test on many groups (d) fix wrong labels
39. 39.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) one group only (c) fewer tests (d) more varied data
40. 40.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias

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