AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 430 · 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 of these adds to bias? (a) balance the data (b) test on many groups (c) add more of the same (d) check who is missing
2. 2.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
3. 3.Which of these adds to bias? (a) copy old unfair choices (b) fix wrong labels (c) ask different people (d) balance the data
4. 4.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) ignoring mistakes (d) more varied data
5. 5.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
6. 6.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
7. 7.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
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: “ignore complaints” adds to bias. (a) True (b) False
10. 10.True or false: “skip testing” adds to bias. (a) False (b) True
11. 11.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
12. 12.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
13. 13.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
14. 14.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
15. 15.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
16. 16.🖼️ 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
17. 17.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
18. 18.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
19. 19.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) its colour (d) the weather
20. 20.🔍 Which is the odd one out? (a) add varied examples (b) ignore complaints (c) add more of the same (d) skip testing
21. 21.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
22. 22.🔍 Which is the odd one out? (a) use one group only (b) ask different people (c) skip testing (d) ignore complaints
23. 23.🔍 Which is the odd one out? (a) fix wrong labels (b) check who is missing (c) use one group only (d) test on many groups
24. 24.🔍 Which is the odd one out? (a) skip testing (b) fix wrong labels (c) add more of the same (d) use one group only
25. 25.🔍 Which is the odd one out? (a) add varied examples (b) ignore complaints (c) balance the data (d) ask different people
26. 26.Which of these helps reduce bias? (a) test on one group only (b) check who is missing (c) ignore complaints (d) add more of the same
27. 27.🗣️ 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) children speak too fast (d) the phone was new
28. 28.Which of these helps reduce bias? (a) skip testing (b) add varied examples (c) ignore complaints (d) add more of the same
29. 29.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
30. 30.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
31. 31.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
32. 32.Which of these adds to bias? (a) ask different people (b) use one group only (c) test on many groups (d) check who is missing
33. 33.True or false: “ask different people” adds to bias. (a) True (b) False
34. 34.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
35. 35.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
36. 36.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
37. 37.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
38. 38.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
39. 39.True or false: “add varied examples” adds to bias. (a) False (b) True
40. 40.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data

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