AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 960 · 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 of many ages and skin tones? (a) one-sided data (b) balanced data
2. 2.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
3. 3.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
4. 4.True or false: “fix wrong labels” adds to bias. (a) True (b) False
5. 5.Which of these adds to bias? (a) copy old unfair choices (b) ask different people (c) add varied examples (d) balance the data
6. 6.True or false: “add more of the same” adds to bias. (a) False (b) True
7. 7.Which of these adds to bias? (a) add varied examples (b) balance the data (c) fix wrong labels (d) add more of the same
8. 8.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) the weather (d) its colour
9. 9.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
10. 10.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
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) True (b) False
12. 12.True or false: “skip testing” helps reduce bias. (a) False (b) True
13. 13.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
14. 14.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
15. 15.Which of these helps reduce bias? (a) add more of the same (b) ignore complaints (c) test on one group only (d) balance the data
16. 16.🔍 Which is the odd one out? (a) test on many groups (b) skip testing (c) add varied examples (d) ask different people
17. 17.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
18. 18.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
19. 19.True or false: “ask different people” helps reduce bias. (a) False (b) True
20. 20.True or false: “use one group only” helps reduce bias. (a) True (b) False
21. 21.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
22. 22.Which of these helps reduce bias? (a) ignore complaints (b) test on one group only (c) copy old unfair choices (d) ask different people
23. 23.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) it was a rainy day (c) few child voices in data (d) children speak too fast
24. 24.Which of these adds to bias? (a) use one group only (b) add varied examples (c) test on many groups (d) ask different people
25. 25.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
26. 26.True or false: “copy old unfair choices” helps reduce bias. (a) False (b) True
27. 27.Which of these adds to bias? (a) check who is missing (b) balance the data (c) add varied examples (d) test on one group only
28. 28.🔍 Which is the odd one out? (a) test on many groups (b) add varied examples (c) copy old unfair choices (d) check who is missing
29. 29.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) False (b) True
30. 30.True or false: “test on one group only” adds to bias. (a) False (b) True
31. 31.True or false: “test on many groups” adds to bias. (a) False (b) True
32. 32.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
33. 33.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
34. 34.Which of these helps reduce bias? (a) add varied examples (b) add more of the same (c) copy old unfair choices (d) ignore complaints
35. 35.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
36. 36.Which of these adds to bias? (a) check who is missing (b) add varied examples (c) ask different people (d) ignore complaints
37. 37.🔍 Which is the odd one out? (a) test on one group only (b) add more of the same (c) ignore complaints (d) test on many groups
38. 38.🔍 Which is the odd one out? (a) add more of the same (b) add varied examples (c) skip testing (d) use one group only
39. 39.🔍 Which is the odd one out? (a) skip testing (b) ask different people (c) add more of the same (d) copy old unfair choices
40. 40.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias

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