AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 867 · 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) ignore complaints (b) add more of the same (c) test on many groups (d) copy old unfair choices
2. 2.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
3. 3.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) few child voices in data (c) it was a rainy day (d) children speak too fast
4. 4.Which of these adds to bias? (a) balance the data (b) test on many groups (c) check who is missing (d) ignore complaints
5. 5.🔍 Which is the odd one out? (a) balance the data (b) copy old unfair choices (c) fix wrong labels (d) check who is missing
6. 6.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
7. 7.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
8. 8.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) its colour (d) the weather
9. 9.Which of these adds to bias? (a) test on many groups (b) use one group only (c) add varied examples (d) balance the data
10. 10.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
11. 11.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) False (b) True
12. 12.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
13. 13.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
14. 14.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
15. 15.True or false: “add varied examples” adds to bias. (a) False (b) True
16. 16.Which of these adds to bias? (a) ask different people (b) skip testing (c) add varied examples (d) balance the data
17. 17.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
18. 18.🔍 Which is the odd one out? (a) add more of the same (b) test on many groups (c) balance the data (d) fix wrong labels
19. 19.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
20. 20.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) more varied data (c) fewer tests (d) one group only
21. 21.True or false: “test on many groups” helps reduce bias. (a) False (b) True
22. 22.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
23. 23.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
24. 24.True or false: “balance the data” adds to bias. (a) False (b) True
25. 25.True or false: “test on many groups” adds to bias. (a) False (b) True
26. 26.Which of these adds to bias? (a) copy old unfair choices (b) balance the data (c) fix wrong labels (d) check who is missing
27. 27.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
28. 28.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
29. 29.Which of these helps reduce bias? (a) use one group only (b) ask different people (c) copy old unfair choices (d) ignore complaints
30. 30.True or false: “test on one group only” helps reduce bias. (a) True (b) False
31. 31.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a strong battery (c) a fair model (d) a broken screen
32. 32.🔍 Which is the odd one out? (a) copy old unfair choices (b) use one group only (c) test on one group only (d) balance the data
33. 33.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
34. 34.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
35. 35.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
36. 36.Which of these helps reduce bias? (a) use one group only (b) copy old unfair choices (c) test on one group only (d) test on many groups
37. 37.True or false: “skip testing” helps reduce bias. (a) True (b) False
38. 38.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
39. 39.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
40. 40.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data

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