AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 174 · 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.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) the weather (d) its colour
2. 2.🔍 Which is the odd one out? (a) add varied examples (b) check who is missing (c) test on many groups (d) use one group only
3. 3.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
4. 4.Which of these helps reduce bias? (a) copy old unfair choices (b) ask different people (c) add more of the same (d) use one group only
5. 5.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
6. 6.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
7. 7.True or false: “test on many groups” adds to bias. (a) False (b) True
8. 8.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
9. 9.Which of these adds to bias? (a) test on one group only (b) check who is missing (c) balance the data (d) test on many groups
10. 10.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
11. 11.True or false: “ignore complaints” adds to bias. (a) False (b) True
12. 12.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
13. 13.🛠️ What helps make an AI system fairer? (a) one group only (b) ignoring mistakes (c) fewer tests (d) more varied data
14. 14.🗣️ 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
15. 15.🔍 Which is the odd one out? (a) ask different people (b) fix wrong labels (c) copy old unfair choices (d) check who is missing
16. 16.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
17. 17.Which of these adds to bias? (a) ask different people (b) test on many groups (c) ignore complaints (d) check who is missing
18. 18.Which of these helps reduce bias? (a) use one group only (b) copy old unfair choices (c) fix wrong labels (d) ignore complaints
19. 19.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
20. 20.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
21. 21.🔍 Which is the odd one out? (a) test on one group only (b) skip testing (c) copy old unfair choices (d) balance the data
22. 22.True or false: “check who is missing” adds to bias. (a) False (b) True
23. 23.True or false: “add varied examples” helps reduce bias. (a) False (b) True
24. 24.True or false: “use one group only” adds to bias. (a) True (b) False
25. 25.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
26. 26.True or false: “balance the data” adds to bias. (a) True (b) False
27. 27.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
28. 28.🔍 Which is the odd one out? (a) check who is missing (b) fix wrong labels (c) add more of the same (d) test on many groups
29. 29.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
30. 30.🔍 Which is the odd one out? (a) copy old unfair choices (b) add varied examples (c) skip testing (d) add more of the same
31. 31.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
32. 32.True or false: “skip testing” helps reduce bias. (a) True (b) False
33. 33.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
34. 34.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
35. 35.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
36. 36.🔍 Which is the odd one out? (a) use one group only (b) copy old unfair choices (c) fix wrong labels (d) skip testing
37. 37.Which of these adds to bias? (a) check who is missing (b) ask different people (c) add varied examples (d) add more of the same
38. 38.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
39. 39.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
40. 40.🔍 Which is the odd one out? (a) add more of the same (b) copy old unfair choices (c) check who is missing (d) use one group only

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