AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 686 · 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 is the odd one out? (a) add more of the same (b) copy old unfair choices (c) ignore complaints (d) balance the data
2. 2.🔍 Which is the odd one out? (a) check who is missing (b) balance the data (c) ignore complaints (d) test on many groups
3. 3.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
4. 4.Which of these adds to bias? (a) ask different people (b) balance the data (c) copy old unfair choices (d) test on many groups
5. 5.🔍 Which is the odd one out? (a) add more of the same (b) ignore complaints (c) skip testing (d) test on many groups
6. 6.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
7. 7.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
8. 8.Which of these adds to bias? (a) ask different people (b) balance the data (c) add varied examples (d) use one group only
9. 9.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its training data (c) its colour (d) its screen size
10. 10.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
11. 11.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
12. 12.Which of these helps reduce bias? (a) add more of the same (b) add varied examples (c) ignore complaints (d) use one group only
13. 13.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
14. 14.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
15. 15.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
16. 16.True or false: “use one group only” adds to bias. (a) False (b) True
17. 17.True or false: “add more of the same” helps reduce bias. (a) True (b) False
18. 18.🔍 Which is the odd one out? (a) ask different people (b) use one group only (c) add more of the same (d) test on one group only
19. 19.🛠️ What helps make an AI system fairer? (a) more varied data (b) fewer tests (c) one group only (d) ignoring mistakes
20. 20.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
21. 21.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
22. 22.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
23. 23.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
24. 24.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
25. 25.🔍 Which is the odd one out? (a) fix wrong labels (b) ignore complaints (c) add more of the same (d) use one group only
26. 26.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
27. 27.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
28. 28.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
29. 29.Which of these adds to bias? (a) check who is missing (b) test on one group only (c) balance the data (d) ask different people
30. 30.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
31. 31.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
32. 32.🔍 Which is the odd one out? (a) balance the data (b) ask different people (c) check who is missing (d) copy old unfair choices
33. 33.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
34. 34.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
35. 35.True or false: “test on one group only” adds to bias. (a) False (b) True
36. 36.🔍 Which is the odd one out? (a) ask different people (b) skip testing (c) test on many groups (d) balance the data
37. 37.Which of these helps reduce bias? (a) skip testing (b) add more of the same (c) balance the data (d) copy old unfair choices
38. 38.🔍 Which is the odd one out? (a) ignore complaints (b) use one group only (c) test on one group only (d) add varied examples
39. 39.Which of these adds to bias? (a) ignore complaints (b) balance the data (c) ask different people (d) fix wrong labels
40. 40.True or false: “test on one group only” helps reduce bias. (a) False (b) True

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