AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 862 · 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.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
2. 2.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
3. 3.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
4. 4.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
5. 5.🔍 Which is the odd one out? (a) check who is missing (b) test on many groups (c) add varied examples (d) copy old unfair choices
6. 6.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) bias from data (c) a broken screen (d) a fair model
7. 7.True or false: “balance the data” helps reduce bias. (a) False (b) True
8. 8.Which of these adds to bias? (a) test on many groups (b) balance the data (c) fix wrong labels (d) copy old unfair choices
9. 9.🔍 Which is the odd one out? (a) add more of the same (b) skip testing (c) use one group only (d) check who is missing
10. 10.True or false: “test on many groups” adds to bias. (a) True (b) False
11. 11.Which of these adds to bias? (a) fix wrong labels (b) check who is missing (c) test on many groups (d) skip testing
12. 12.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
13. 13.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
14. 14.🔍 Which is the odd one out? (a) check who is missing (b) add more of the same (c) balance the data (d) add varied examples
15. 15.🔍 Which is the odd one out? (a) ask different people (b) test on one group only (c) use one group only (d) ignore complaints
16. 16.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
17. 17.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False
18. 18.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
19. 19.🛠️ What helps make an AI system fairer? (a) one group only (b) ignoring mistakes (c) more varied data (d) fewer tests
20. 20.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
21. 21.Which of these helps reduce bias? (a) test on many groups (b) copy old unfair choices (c) test on one group only (d) use one group only
22. 22.True or false: “check who is missing” adds to bias. (a) False (b) True
23. 23.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
24. 24.🔍 Which is the odd one out? (a) add varied examples (b) ignore complaints (c) skip testing (d) use one group only
25. 25.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
26. 26.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
27. 27.Which of these helps reduce bias? (a) balance the data (b) add more of the same (c) ignore complaints (d) test on one group only
28. 28.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) the weather (d) its training data
29. 29.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
30. 30.True or false: “use one group only” adds to bias. (a) False (b) True
31. 31.🔍 Which is the odd one out? (a) add varied examples (b) balance the data (c) test on one group only (d) test on many groups
32. 32.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) fix wrong labels (d) ignore complaints
33. 33.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) few child voices in data (c) the phone was new (d) children speak too fast
34. 34.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
35. 35.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
36. 36.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
37. 37.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
38. 38.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
39. 39.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
40. 40.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True

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