AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 353 · 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.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) it was a rainy day (c) the phone was new (d) few child voices in data
2. 2.Which of these adds to bias? (a) skip testing (b) test on many groups (c) fix wrong labels (d) balance the data
3. 3.True or false: “balance the data” helps reduce bias. (a) False (b) True
4. 4.Which of these helps reduce bias? (a) copy old unfair choices (b) ask different people (c) add more of the same (d) skip testing
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: mangoes both raw and ripe? (a) one-sided data (b) balanced data
7. 7.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
8. 8.🛠️ What helps make an AI system fairer? (a) fewer tests (b) more varied data (c) one group only (d) ignoring mistakes
9. 9.🔍 Which is the odd one out? (a) balance the data (b) add varied examples (c) use one group only (d) fix wrong labels
10. 10.True or false: “ask different people” 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.🔍 Which is the odd one out? (a) ask different people (b) fix wrong labels (c) test on many groups (d) add more of the same
13. 13.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
14. 14.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
15. 15.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
16. 16.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
17. 17.True or false: “fix wrong labels” adds to bias. (a) True (b) False
18. 18.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
19. 19.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
20. 20.🔍 Which is the odd one out? (a) skip testing (b) test on one group only (c) ask different people (d) copy old unfair choices
21. 21.Which of these adds to bias? (a) ask different people (b) use one group only (c) balance the data (d) add varied examples
22. 22.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
23. 23.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a broken screen (c) bias from data (d) a fair model
24. 24.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
25. 25.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
26. 26.🔍 Which is the odd one out? (a) balance the data (b) fix wrong labels (c) test on many groups (d) copy old unfair choices
27. 27.Which of these helps reduce bias? (a) skip testing (b) test on many groups (c) test on one group only (d) ignore complaints
28. 28.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
29. 29.Which of these adds to bias? (a) fix wrong labels (b) ask different people (c) ignore complaints (d) add varied examples
30. 30.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
31. 31.True or false: “test on many groups” adds to bias. (a) True (b) False
32. 32.True or false: “check who is missing” adds to bias. (a) True (b) False
33. 33.🔍 Which is the odd one out? (a) add varied examples (b) use one group only (c) copy old unfair choices (d) skip testing
34. 34.Which of these helps reduce bias? (a) skip testing (b) test on one group only (c) use one group only (d) balance the data
35. 35.Which of these helps reduce bias? (a) add varied examples (b) add more of the same (c) use one group only (d) skip testing
36. 36.🔍 Which is the odd one out? (a) use one group only (b) copy old unfair choices (c) test on many groups (d) ignore complaints
37. 37.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
38. 38.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
39. 39.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its training data (c) its colour (d) its screen size
40. 40.🔍 Which is the odd one out? (a) ask different people (b) skip testing (c) add varied examples (d) test on many groups

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