AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 350 · 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: photos of people from only one age group? (a) balanced data (b) one-sided data
2. 2.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
3. 3.Which of these adds to bias? (a) add varied examples (b) balance the data (c) test on many groups (d) copy old unfair choices
4. 4.🔍 Which is the odd one out? (a) fix wrong labels (b) test on one group only (c) check who is missing (d) ask different people
5. 5.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
6. 6.🔍 Which is the odd one out? (a) skip testing (b) balance the data (c) test on one group only (d) ignore complaints
7. 7.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
8. 8.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a broken screen (c) a strong battery (d) bias from data
9. 9.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
10. 10.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
11. 11.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
12. 12.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
13. 13.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
14. 14.Which of these helps reduce bias? (a) test on one group only (b) fix wrong labels (c) copy old unfair choices (d) use one group only
15. 15.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
16. 16.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
17. 17.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
18. 18.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
19. 19.🔍 Which is the odd one out? (a) skip testing (b) test on one group only (c) add varied examples (d) ignore complaints
20. 20.True or false: “add more of the same” helps reduce bias. (a) True (b) False
21. 21.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
22. 22.🔍 Which is the odd one out? (a) ask different people (b) ignore complaints (c) test on many groups (d) fix wrong labels
23. 23.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
24. 24.True or false: “skip testing” helps reduce bias. (a) True (b) False
25. 25.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
26. 26.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its colour (c) its training data (d) the weather
27. 27.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
28. 28.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
29. 29.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
30. 30.Which of these adds to bias? (a) ask different people (b) test on many groups (c) fix wrong labels (d) use one group only
31. 31.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
32. 32.Which of these helps reduce bias? (a) add varied examples (b) test on one group only (c) use one group only (d) ignore complaints
33. 33.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
34. 34.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) it was a rainy day (c) children speak too fast (d) the phone was new
35. 35.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
36. 36.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
37. 37.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
38. 38.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) more varied data (d) ignoring mistakes
39. 39.Which of these helps reduce bias? (a) add more of the same (b) check who is missing (c) test on one group only (d) ignore complaints
40. 40.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data

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