AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 381 · 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) skip testing (b) add varied examples (c) test on many groups (d) fix wrong labels
2. 2.🗣️ 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
3. 3.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
4. 4.True or false: “skip testing” helps reduce bias. (a) False (b) True
5. 5.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
6. 6.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
7. 7.🔍 Which is the odd one out? (a) skip testing (b) use one group only (c) copy old unfair choices (d) check who is missing
8. 8.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
9. 9.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
10. 10.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
11. 11.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
12. 12.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
13. 13.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
14. 14.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
15. 15.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
16. 16.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its training data (d) its screen size
17. 17.True or false: “use one group only” helps reduce bias. (a) True (b) False
18. 18.Which of these helps reduce bias? (a) add more of the same (b) ignore complaints (c) use one group only (d) balance the data
19. 19.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
20. 20.🔍 Which is the odd one out? (a) check who is missing (b) add more of the same (c) ask different people (d) add varied examples
21. 21.True or false: “ignore complaints” adds to bias. (a) True (b) False
22. 22.Which of these helps reduce bias? (a) copy old unfair choices (b) add more of the same (c) check who is missing (d) ignore complaints
23. 23.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
24. 24.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
25. 25.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
26. 26.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
27. 27.🔍 Which is the odd one out? (a) use one group only (b) ignore complaints (c) test on many groups (d) copy old unfair choices
28. 28.True or false: photos of people from only one age group 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) fix wrong labels (c) copy old unfair choices (d) add varied examples
30. 30.🛠️ What helps make an AI system fairer? (a) more varied data (b) one group only (c) ignoring mistakes (d) fewer tests
31. 31.Which of these adds to bias? (a) ask different people (b) test on many groups (c) skip testing (d) balance the data
32. 32.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
33. 33.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
34. 34.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
35. 35.🔍 Which is the odd one out? (a) add more of the same (b) add varied examples (c) ignore complaints (d) copy old unfair choices
36. 36.True or false: “add varied examples” adds to bias. (a) False (b) True
37. 37.🔍 Which is the odd one out? (a) ask different people (b) test on one group only (c) ignore complaints (d) use one group only
38. 38.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
39. 39.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
40. 40.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias

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