AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 741 · 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) use one group only (b) test on one group only (c) balance the data (d) ignore complaints
2. 2.🗣️ 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
3. 3.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
4. 4.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) False (b) True
5. 5.🔍 Which is the odd one out? (a) ignore complaints (b) add more of the same (c) copy old unfair choices (d) check who is missing
6. 6.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
7. 7.True or false: “add varied examples” adds to bias. (a) True (b) False
8. 8.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) the weather (d) its colour
9. 9.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
10. 10.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
11. 11.🔍 Which is the odd one out? (a) ignore complaints (b) add varied examples (c) balance the data (d) test on many groups
12. 12.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
13. 13.Which of these adds to bias? (a) add varied examples (b) add more of the same (c) balance the data (d) test on many groups
14. 14.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
15. 15.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
16. 16.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
17. 17.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
18. 18.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
19. 19.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
20. 20.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
21. 21.True or false: “test on one group only” adds to bias. (a) False (b) True
22. 22.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
23. 23.🛠️ What helps make an AI system fairer? (a) fewer tests (b) ignoring mistakes (c) one group only (d) more varied data
24. 24.Which of these adds to bias? (a) add varied examples (b) balance the data (c) skip testing (d) fix wrong labels
25. 25.Which of these adds to bias? (a) fix wrong labels (b) test on one group only (c) check who is missing (d) test on many groups
26. 26.Which of these helps reduce bias? (a) use one group only (b) add varied examples (c) copy old unfair choices (d) test on one group only
27. 27.🔍 Which is the odd one out? (a) test on many groups (b) fix wrong labels (c) balance the data (d) test on one group only
28. 28.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
29. 29.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
30. 30.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
31. 31.Which of these adds to bias? (a) use one group only (b) check who is missing (c) add varied examples (d) ask different people
32. 32.🔍 Which is the odd one out? (a) fix wrong labels (b) skip testing (c) balance the data (d) test on many groups
33. 33.True or false: “fix wrong labels” adds to bias. (a) False (b) True
34. 34.🔍 Which is the odd one out? (a) ignore complaints (b) ask different people (c) skip testing (d) test on one group only
35. 35.True or false: “add more of the same” helps reduce bias. (a) True (b) False
36. 36.True or false: “check who is missing” helps reduce bias. (a) True (b) False
37. 37.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
38. 38.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a strong battery (c) a fair model (d) bias from data
39. 39.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
40. 40.Which of these helps reduce bias? (a) test on one group only (b) ignore complaints (c) test on many groups (d) copy old unfair choices

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