AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 503 · 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.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
2. 2.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
3. 3.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
4. 4.🛠️ What helps make an AI system fairer? (a) fewer tests (b) more varied data (c) ignoring mistakes (d) one group only
5. 5.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
6. 6.True or false: “check who is missing” adds to bias. (a) False (b) True
7. 7.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its training data (c) its colour (d) its screen size
8. 8.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
9. 9.Which of these helps reduce bias? (a) skip testing (b) ignore complaints (c) copy old unfair choices (d) fix wrong labels
10. 10.Which of these adds to bias? (a) check who is missing (b) ask different people (c) skip testing (d) balance the data
11. 11.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
12. 12.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
13. 13.Which of these helps reduce bias? (a) copy old unfair choices (b) ignore complaints (c) add more of the same (d) balance the data
14. 14.🔍 Which is the odd one out? (a) add varied examples (b) add more of the same (c) ask different people (d) fix wrong labels
15. 15.True or false: “test on many groups” adds to bias. (a) True (b) False
16. 16.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
17. 17.🔍 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
18. 18.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
19. 19.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
20. 20.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
21. 21.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
22. 22.Which of these helps reduce bias? (a) ignore complaints (b) add varied examples (c) skip testing (d) copy old unfair choices
23. 23.🔍 Which is the odd one out? (a) test on many groups (b) ignore complaints (c) use one group only (d) test on one group only
24. 24.True or false: “use one group only” helps reduce bias. (a) False (b) True
25. 25.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) the phone was new (c) children speak too fast (d) few child voices in data
26. 26.Which of these adds to bias? (a) ask different people (b) fix wrong labels (c) ignore complaints (d) add varied examples
27. 27.True or false: “test on many groups” helps reduce bias. (a) True (b) False
28. 28.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
29. 29.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a broken screen (c) bias from data (d) a fair model
30. 30.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
31. 31.Which of these adds to bias? (a) test on many groups (b) check who is missing (c) add varied examples (d) use one group only
32. 32.Which of these helps reduce bias? (a) skip testing (b) add more of the same (c) ignore complaints (d) test on many groups
33. 33.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) False (b) True
34. 34.🔍 Which is the odd one out? (a) ask different people (b) test on many groups (c) copy old unfair choices (d) fix wrong labels
35. 35.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
36. 36.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
37. 37.Which of these adds to bias? (a) copy old unfair choices (b) check who is missing (c) test on many groups (d) add varied examples
38. 38.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
39. 39.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
40. 40.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data

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