AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 192 · 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: voices of children and adults from many regions? (a) balanced data (b) one-sided data
2. 2.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
3. 3.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
4. 4.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
5. 5.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
6. 6.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
7. 7.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
8. 8.Which of these adds to bias? (a) balance the data (b) skip testing (c) fix wrong labels (d) ask different people
9. 9.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
10. 10.🔍 Which is the odd one out? (a) ask different people (b) add more of the same (c) test on many groups (d) fix wrong labels
11. 11.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
12. 12.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) False (b) True
13. 13.Which of these adds to bias? (a) test on many groups (b) test on one group only (c) add varied examples (d) fix wrong labels
14. 14.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
15. 15.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
16. 16.Which of these helps reduce bias? (a) skip testing (b) test on one group only (c) ask different people (d) copy old unfair choices
17. 17.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
18. 18.🔍 Which is the odd one out? (a) ignore complaints (b) check who is missing (c) test on one group only (d) skip testing
19. 19.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
20. 20.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
21. 21.True or false: “test on many groups” helps reduce bias. (a) True (b) False
22. 22.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) True (b) False
23. 23.True or false: “ask different people” helps reduce bias. (a) False (b) True
24. 24.🔍 Which is the odd one out? (a) fix wrong labels (b) ignore complaints (c) copy old unfair choices (d) test on one group only
25. 25.Which of these helps reduce bias? (a) use one group only (b) skip testing (c) copy old unfair choices (d) check who is missing
26. 26.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) its training data (d) the weather
27. 27.🔍 Which is the odd one out? (a) test on many groups (b) use one group only (c) ask different people (d) add varied examples
28. 28.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
29. 29.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
30. 30.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
31. 31.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
32. 32.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
33. 33.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
34. 34.Which of these adds to bias? (a) test on many groups (b) add varied examples (c) ask different people (d) use one group only
35. 35.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
36. 36.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
37. 37.True or false: “balance the data” helps reduce bias. (a) True (b) False
38. 38.🔍 Which is the odd one out? (a) ask different people (b) test on many groups (c) copy old unfair choices (d) fix wrong labels
39. 39.True or false: “check who is missing” helps reduce bias. (a) True (b) False
40. 40.🖼️ 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

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