AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 778 · 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: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
2. 2.Which of these adds to bias? (a) balance the data (b) add varied examples (c) check who is missing (d) use one group only
3. 3.🖼️ 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
4. 4.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) one group only (c) more varied data (d) fewer tests
5. 5.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
6. 6.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
7. 7.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
8. 8.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
9. 9.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
10. 10.🔍 Which is the odd one out? (a) copy old unfair choices (b) add more of the same (c) test on many groups (d) ignore complaints
11. 11.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
12. 12.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
13. 13.Which of these adds to bias? (a) test on one group only (b) check who is missing (c) test on many groups (d) fix wrong labels
14. 14.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
15. 15.Which of these helps reduce bias? (a) ignore complaints (b) add more of the same (c) test on one group only (d) check who is missing
16. 16.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
17. 17.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
18. 18.🔍 Which is the odd one out? (a) ignore complaints (b) test on one group only (c) add varied examples (d) use one group only
19. 19.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
20. 20.🔍 Which is the odd one out? (a) skip testing (b) ask different people (c) copy old unfair choices (d) add more of the same
21. 21.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
22. 22.🔍 Which is the odd one out? (a) ignore complaints (b) balance the data (c) check who is missing (d) ask different people
23. 23.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
24. 24.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) False (b) True
25. 25.Which of these adds to bias? (a) copy old unfair choices (b) add varied examples (c) balance the data (d) fix wrong labels
26. 26.🔍 Which is the odd one out? (a) use one group only (b) ignore complaints (c) balance the data (d) skip testing
27. 27.Which of these adds to bias? (a) ask different people (b) check who is missing (c) add varied examples (d) skip testing
28. 28.🔍 Which is the odd one out? (a) use one group only (b) add varied examples (c) test on many groups (d) check who is missing
29. 29.Which of these helps reduce bias? (a) test on one group only (b) balance the data (c) copy old unfair choices (d) skip testing
30. 30.True or false: “skip testing” helps reduce bias. (a) False (b) True
31. 31.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
32. 32.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
33. 33.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) children speak too fast (c) few child voices in data (d) it was a rainy day
34. 34.True or false: “add more of the same” helps reduce bias. (a) True (b) False
35. 35.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
36. 36.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
37. 37.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its colour (c) the weather (d) its screen size
38. 38.True or false: “use one group only” adds to bias. (a) True (b) False
39. 39.🔍 Which is the odd one out? (a) ask different people (b) check who is missing (c) add varied examples (d) copy old unfair choices
40. 40.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data

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