AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 410 · 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) check who is missing (b) skip testing (c) test on one group only (d) copy old unfair choices
2. 2.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
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.Which of these adds to bias? (a) add varied examples (b) test on many groups (c) use one group only (d) ask different people
5. 5.🔍 Which is the odd one out? (a) add varied examples (b) fix wrong labels (c) check who is missing (d) use one group only
6. 6.True or false: “ask different people” adds to bias. (a) False (b) True
7. 7.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
8. 8.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
9. 9.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its training data (d) its screen size
10. 10.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
11. 11.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) children speak too fast (c) it was a rainy day (d) the phone was new
12. 12.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
13. 13.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
14. 14.🔍 Which is the odd one out? (a) add more of the same (b) skip testing (c) test on one group only (d) fix wrong labels
15. 15.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) add more of the same (d) balance the data
16. 16.🔍 Which is the odd one out? (a) test on one group only (b) test on many groups (c) ask different people (d) add varied examples
17. 17.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
18. 18.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
19. 19.Which of these helps reduce bias? (a) fix wrong labels (b) copy old unfair choices (c) add more of the same (d) use one group only
20. 20.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
21. 21.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
22. 22.True or false: “fix wrong labels” adds to bias. (a) True (b) False
23. 23.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
24. 24.Which of these adds to bias? (a) test on many groups (b) add more of the same (c) balance the data (d) check who is missing
25. 25.Which of these helps reduce bias? (a) copy old unfair choices (b) use one group only (c) skip testing (d) ask different people
26. 26.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
27. 27.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
28. 28.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
29. 29.🔍 Which is the odd one out? (a) use one group only (b) check who is missing (c) test on one group only (d) skip testing
30. 30.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) False (b) True
31. 31.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
32. 32.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False
33. 33.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
34. 34.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
35. 35.🔍 Which is the odd one out? (a) use one group only (b) ask different people (c) ignore complaints (d) copy old unfair choices
36. 36.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
37. 37.True or false: “test on one group only” adds to bias. (a) False (b) True
38. 38.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
39. 39.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
40. 40.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias

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