AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 136 · 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.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
2. 2.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
3. 3.🔍 Which is the odd one out? (a) balance the data (b) add varied examples (c) use one group only (d) ask different people
4. 4.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
5. 5.🔍 Which is the odd one out? (a) use one group only (b) test on one group only (c) skip testing (d) balance the data
6. 6.🔍 Which is the odd one out? (a) test on one group only (b) check who is missing (c) copy old unfair choices (d) use one group only
7. 7.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
8. 8.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
9. 9.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
10. 10.🛠️ What helps make an AI system fairer? (a) fewer tests (b) ignoring mistakes (c) one group only (d) more varied data
11. 11.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) False (b) True
12. 12.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
13. 13.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
14. 14.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
15. 15.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
16. 16.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its training data (c) the weather (d) its screen size
17. 17.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
18. 18.Which of these adds to bias? (a) test on many groups (b) fix wrong labels (c) copy old unfair choices (d) balance the data
19. 19.True or false: “test on one group only” adds to bias. (a) False (b) True
20. 20.True or false: “test on many groups” helps reduce bias. (a) True (b) False
21. 21.⚖️ One-sided or balanced data: mangoes both raw and ripe? (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.🔍 Which is the odd one out? (a) ignore complaints (b) test on many groups (c) test on one group only (d) copy old unfair choices
24. 24.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a broken screen (c) a fair model (d) a strong battery
25. 25.🔍 Which is the odd one out? (a) test on many groups (b) add varied examples (c) add more of the same (d) balance the data
26. 26.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
27. 27.Which of these adds to bias? (a) check who is missing (b) test on one group only (c) add varied examples (d) balance the data
28. 28.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
29. 29.🔍 Which is the odd one out? (a) check who is missing (b) balance the data (c) add varied examples (d) skip testing
30. 30.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) few child voices in data (c) it was a rainy day (d) the phone was new
31. 31.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
32. 32.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
33. 33.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
34. 34.True or false: “test on many groups” adds to bias. (a) True (b) False
35. 35.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
36. 36.True or false: “balance the data” adds to bias. (a) True (b) False
37. 37.Which of these helps reduce bias? (a) add varied examples (b) use one group only (c) copy old unfair choices (d) add more of the same
38. 38.True or false: “skip testing” helps reduce bias. (a) False (b) True
39. 39.🔍 Which is the odd one out? (a) test on one group only (b) add varied examples (c) ignore complaints (d) copy old unfair choices
40. 40.Which of these adds to bias? (a) check who is missing (b) fix wrong labels (c) skip testing (d) ask different people

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