AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 834 · 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.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its colour (c) the weather (d) its training data
2. 2.Which of these adds to bias? (a) ask different people (b) check who is missing (c) fix wrong labels (d) ignore complaints
3. 3.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
4. 4.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
5. 5.Which of these helps reduce bias? (a) add more of the same (b) ignore complaints (c) fix wrong labels (d) test on one group only
6. 6.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
7. 7.True or false: “fix wrong labels” adds to bias. (a) False (b) True
8. 8.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
9. 9.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
10. 10.🔍 Which is the odd one out? (a) add varied examples (b) copy old unfair choices (c) use one group only (d) ignore complaints
11. 11.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
12. 12.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
13. 13.🔍 Which is the odd one out? (a) test on one group only (b) use one group only (c) add more of the same (d) check who is missing
14. 14.Which of these helps reduce bias? (a) add more of the same (b) skip testing (c) copy old unfair choices (d) add varied examples
15. 15.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
16. 16.True or false: “check who is missing” adds to bias. (a) True (b) False
17. 17.True or false: “use one group only” adds to bias. (a) True (b) False
18. 18.🗣️ 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) the phone was new (d) children speak too fast
19. 19.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
20. 20.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
21. 21.True or false: “test on one group only” helps reduce bias. (a) False (b) True
22. 22.Which of these adds to bias? (a) test on one group only (b) test on many groups (c) fix wrong labels (d) check who is missing
23. 23.🔍 Which is the odd one out? (a) balance the data (b) test on many groups (c) copy old unfair choices (d) fix wrong labels
24. 24.🔍 Which is the odd one out? (a) ask different people (b) fix wrong labels (c) skip testing (d) balance the data
25. 25.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) True (b) False
26. 26.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
27. 27.Which of these adds to bias? (a) balance the data (b) ask different people (c) use one group only (d) add varied examples
28. 28.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a strong battery (c) bias from data (d) a broken screen
29. 29.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
30. 30.True or false: “use one group only” helps reduce bias. (a) True (b) False
31. 31.Which of these helps reduce bias? (a) add more of the same (b) ignore complaints (c) skip testing (d) balance the data
32. 32.Which of these adds to bias? (a) ask different people (b) fix wrong labels (c) add varied examples (d) copy old unfair choices
33. 33.Which of these helps reduce bias? (a) ask different people (b) skip testing (c) copy old unfair choices (d) use one group only
34. 34.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
35. 35.🔍 Which is the odd one out? (a) use one group only (b) add varied examples (c) check who is missing (d) balance the data
36. 36.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
37. 37.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
38. 38.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
39. 39.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
40. 40.Which of these adds to bias? (a) add more of the same (b) balance the data (c) fix wrong labels (d) add varied examples

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