AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 849 · 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: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
2. 2.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
3. 3.🔍 Which is the odd one out? (a) use one group only (b) ignore complaints (c) fix wrong labels (d) add more of the same
4. 4.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
5. 5.🔍 Which is the odd one out? (a) skip testing (b) test on many groups (c) check who is missing (d) add varied examples
6. 6.True or false: “fix wrong labels” adds to bias. (a) True (b) False
7. 7.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) the weather (d) its training data
8. 8.🔍 Which is the odd one out? (a) test on many groups (b) fix wrong labels (c) balance the data (d) use one group only
9. 9.True or false: “test on many groups” helps reduce bias. (a) True (b) False
10. 10.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
11. 11.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) children speak too fast (c) the phone was new (d) it was a rainy day
12. 12.True or false: “check who is missing” helps reduce bias. (a) False (b) True
13. 13.Which of these helps reduce bias? (a) balance the data (b) use one group only (c) test on one group only (d) ignore complaints
14. 14.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
15. 15.🔍 Which is the odd one out? (a) balance the data (b) add varied examples (c) add more of the same (d) check who is missing
16. 16.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) check who is missing (d) test on one group only
17. 17.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) False (b) True
18. 18.True or false: “test on one group only” helps reduce bias. (a) True (b) False
19. 19.Which of these adds to bias? (a) check who is missing (b) fix wrong labels (c) add more of the same (d) add varied examples
20. 20.Which of these helps reduce bias? (a) add more of the same (b) copy old unfair choices (c) ignore complaints (d) add varied examples
21. 21.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
22. 22.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
23. 23.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
24. 24.True or false: “balance the data” adds to bias. (a) False (b) True
25. 25.Which of these helps reduce bias? (a) test on one group only (b) skip testing (c) copy old unfair choices (d) test on many groups
26. 26.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
27. 27.🛠️ What helps make an AI system fairer? (a) fewer tests (b) ignoring mistakes (c) more varied data (d) one group only
28. 28.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
29. 29.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
30. 30.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
31. 31.Which of these adds to bias? (a) add varied examples (b) balance the data (c) copy old unfair choices (d) test on many groups
32. 32.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
33. 33.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) True (b) False
34. 34.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
35. 35.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
36. 36.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
37. 37.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
38. 38.🔍 Which is the odd one out? (a) check who is missing (b) test on many groups (c) fix wrong labels (d) copy old unfair choices
39. 39.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
40. 40.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a fair model (c) a strong battery (d) bias from data

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