AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 632 · 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 varied examples (b) test on one group only (c) use one group only (d) copy old unfair choices
2. 2.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
3. 3.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
4. 4.Which of these adds to bias? (a) ask different people (b) check who is missing (c) test on one group only (d) balance the data
5. 5.True or false: “fix wrong labels” adds to bias. (a) False (b) True
6. 6.🔍 Which is the odd one out? (a) add more of the same (b) add varied examples (c) check who is missing (d) test on many groups
7. 7.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
8. 8.🛠️ What helps make an AI system fairer? (a) one group only (b) more varied data (c) fewer tests (d) ignoring mistakes
9. 9.Which of these adds to bias? (a) ignore complaints (b) test on many groups (c) add varied examples (d) check who is missing
10. 10.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
11. 11.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
12. 12.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
13. 13.Which of these adds to bias? (a) add varied examples (b) test on many groups (c) use one group only (d) balance the data
14. 14.Which of these helps reduce bias? (a) use one group only (b) test on one group only (c) check who is missing (d) add more of the same
15. 15.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a strong battery (c) a broken screen (d) bias from data
16. 16.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
17. 17.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
18. 18.Which of these helps reduce bias? (a) copy old unfair choices (b) skip testing (c) test on one group only (d) balance the data
19. 19.True or false: “ask different people” adds to bias. (a) False (b) True
20. 20.🔍 Which is the odd one out? (a) test on many groups (b) use one group only (c) check who is missing (d) add varied examples
21. 21.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
22. 22.🔍 Which is the odd one out? (a) use one group only (b) add more of the same (c) test on one group only (d) fix wrong labels
23. 23.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
24. 24.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) it was a rainy day (c) few child voices in data (d) children speak too fast
25. 25.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
26. 26.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its colour (c) the weather (d) its screen size
27. 27.True or false: “add more of the same” adds to bias. (a) True (b) False
28. 28.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
29. 29.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
30. 30.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
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: stories where every doctor is a man? (a) balanced data (b) one-sided data
33. 33.True or false: “copy old unfair choices” helps reduce bias. (a) False (b) True
34. 34.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
35. 35.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
36. 36.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
37. 37.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
38. 38.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
39. 39.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
40. 40.Which of these helps reduce bias? (a) use one group only (b) ignore complaints (c) copy old unfair choices (d) ask different people

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