AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 926 · 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 adds to bias? (a) skip testing (b) ask different people (c) add varied examples (d) fix wrong labels
2. 2.Which of these helps reduce bias? (a) copy old unfair choices (b) test on one group only (c) skip testing (d) ask different people
3. 3.Which of these adds to bias? (a) balance the data (b) fix wrong labels (c) test on one group only (d) ask different people
4. 4.Which of these helps reduce bias? (a) use one group only (b) add varied examples (c) skip testing (d) test on one group only
5. 5.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
6. 6.🔍 Which is the odd one out? (a) ask different people (b) add varied examples (c) copy old unfair choices (d) check who is missing
7. 7.True or false: “add more of the same” helps reduce bias. (a) True (b) False
8. 8.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
9. 9.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
10. 10.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
11. 11.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
12. 12.🔍 Which is the odd one out? (a) copy old unfair choices (b) use one group only (c) ignore complaints (d) add varied examples
13. 13.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) True (b) False
14. 14.True or false: “test on one group only” adds to bias. (a) False (b) True
15. 15.Which of these helps reduce bias? (a) test on one group only (b) fix wrong labels (c) skip testing (d) use one group only
16. 16.True or false: “test on many groups” helps reduce bias. (a) False (b) True
17. 17.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) skip testing (d) fix wrong labels
18. 18.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
19. 19.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a broken screen (c) a fair model (d) bias from data
20. 20.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
21. 21.True or false: “add varied examples” adds to bias. (a) False (b) True
22. 22.Which of these helps reduce bias? (a) copy old unfair choices (b) test on many groups (c) add more of the same (d) ignore complaints
23. 23.Which of these adds to bias? (a) add varied examples (b) balance the data (c) add more of the same (d) fix wrong labels
24. 24.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
25. 25.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its training data (d) its screen size
26. 26.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
27. 27.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
28. 28.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
29. 29.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
30. 30.🔍 Which is the odd one out? (a) ask different people (b) check who is missing (c) fix wrong labels (d) add more of the same
31. 31.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
32. 32.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
33. 33.🔍 Which is the odd one out? (a) copy old unfair choices (b) add more of the same (c) skip testing (d) fix wrong labels
34. 34.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
35. 35.Which of these helps reduce bias? (a) add more of the same (b) copy old unfair choices (c) use one group only (d) check who is missing
36. 36.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
37. 37.True or false: “ask different people” helps reduce bias. (a) True (b) False
38. 38.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
39. 39.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
40. 40.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data

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