AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 987 · 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.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) more varied data (d) ignoring mistakes
2. 2.True or false: “test on one group only” helps reduce bias. (a) True (b) False
3. 3.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
4. 4.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
5. 5.🔍 Which is the odd one out? (a) use one group only (b) add more of the same (c) skip testing (d) check who is missing
6. 6.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
7. 7.True or false: “skip testing” helps reduce bias. (a) False (b) True
8. 8.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its screen size (c) the weather (d) its colour
9. 9.Which of these adds to bias? (a) copy old unfair choices (b) balance the data (c) add varied examples (d) test on many groups
10. 10.Which of these helps reduce bias? (a) test on one group only (b) balance the data (c) skip testing (d) use one group only
11. 11.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
12. 12.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a strong battery (c) a fair model (d) a broken screen
13. 13.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
14. 14.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) children speak too fast (c) it was a rainy day (d) few child voices in data
15. 15.🔍 Which is the odd one out? (a) balance the data (b) test on one group only (c) add varied examples (d) check who is missing
16. 16.Which of these adds to bias? (a) add varied examples (b) test on many groups (c) check who is missing (d) test on one group only
17. 17.Which of these helps reduce bias? (a) ignore complaints (b) use one group only (c) ask different people (d) test on one group only
18. 18.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
19. 19.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
20. 20.Which of these helps reduce bias? (a) ignore complaints (b) add varied examples (c) skip testing (d) use one group only
21. 21.Which of these helps reduce bias? (a) copy old unfair choices (b) test on many groups (c) ignore complaints (d) add more of the same
22. 22.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
23. 23.🔍 Which is the odd one out? (a) check who is missing (b) add varied examples (c) ask different people (d) ignore complaints
24. 24.Which of these helps reduce bias? (a) copy old unfair choices (b) check who is missing (c) ignore complaints (d) add more of the same
25. 25.🔍 Which is the odd one out? (a) check who is missing (b) add varied examples (c) ask different people (d) use one group only
26. 26.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
27. 27.🔍 Which is the odd one out? (a) balance the data (b) add more of the same (c) test on many groups (d) fix wrong labels
28. 28.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
29. 29.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
30. 30.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
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.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
33. 33.True or false: “check who is missing” helps reduce bias. (a) True (b) False
34. 34.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
35. 35.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
36. 36.Which of these adds to bias? (a) ask different people (b) add varied examples (c) balance the data (d) add more of the same
37. 37.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
38. 38.🔍 Which is the odd one out? (a) add more of the same (b) test on one group only (c) ignore complaints (d) test on many groups
39. 39.Which of these helps reduce bias? (a) ignore complaints (b) use one group only (c) fix wrong labels (d) copy old unfair choices
40. 40.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True

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