AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 257 · 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.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
2. 2.🗣️ 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) few child voices in data (d) the phone was new
3. 3.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
4. 4.True or false: “balance the data” adds to bias. (a) True (b) False
5. 5.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
6. 6.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
7. 7.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
8. 8.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its screen size (c) its colour (d) its training data
9. 9.Which of these adds to bias? (a) fix wrong labels (b) check who is missing (c) balance the data (d) skip testing
10. 10.🔍 Which is the odd one out? (a) check who is missing (b) ignore complaints (c) test on many groups (d) add varied examples
11. 11.🔍 Which is the odd one out? (a) add varied examples (b) check who is missing (c) add more of the same (d) balance the data
12. 12.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
13. 13.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
14. 14.🔍 Which is the odd one out? (a) ask different people (b) ignore complaints (c) copy old unfair choices (d) skip testing
15. 15.Which of these adds to bias? (a) balance the data (b) fix wrong labels (c) use one group only (d) test on many groups
16. 16.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
17. 17.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
18. 18.Which of these helps reduce bias? (a) copy old unfair choices (b) skip testing (c) add more of the same (d) balance the data
19. 19.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
20. 20.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
21. 21.True or false: “test on one group only” helps reduce bias. (a) True (b) False
22. 22.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
23. 23.🔍 Which is the odd one out? (a) use one group only (b) check who is missing (c) add more of the same (d) copy old unfair choices
24. 24.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
25. 25.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
26. 26.True or false: “test on many groups” helps reduce bias. (a) True (b) False
27. 27.Which of these helps reduce bias? (a) use one group only (b) add more of the same (c) test on many groups (d) skip testing
28. 28.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
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.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
31. 31.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
32. 32.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a strong battery (c) a broken screen (d) bias from data
33. 33.🔍 Which is the odd one out? (a) add varied examples (b) ignore complaints (c) use one group only (d) copy old unfair choices
34. 34.Which of these adds to bias? (a) balance the data (b) add varied examples (c) fix wrong labels (d) add more of the same
35. 35.Which of these helps reduce bias? (a) add more of the same (b) test on one group only (c) add varied examples (d) copy old unfair choices
36. 36.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
37. 37.🔍 Which is the odd one out? (a) test on many groups (b) fix wrong labels (c) check who is missing (d) test on one group only
38. 38.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
39. 39.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
40. 40.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data

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