AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 923 · 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.🔍 Which is the odd one out? (a) test on many groups (b) skip testing (c) ask different people (d) add varied examples
2. 2.🔍 Which is the odd one out? (a) balance the data (b) test on many groups (c) add more of the same (d) ask different people
3. 3.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
4. 4.True or false: “add varied examples” adds to bias. (a) True (b) False
5. 5.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) fewer tests (c) one group only (d) more varied data
6. 6.Which of these helps reduce bias? (a) test on one group only (b) ignore complaints (c) add more of the same (d) check who is missing
7. 7.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
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.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
10. 10.🔍 Which is the odd one out? (a) fix wrong labels (b) test on many groups (c) copy old unfair choices (d) add varied examples
11. 11.True or false: “skip testing” adds to bias. (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) fix wrong labels (b) add varied examples (c) balance the data (d) ignore complaints
14. 14.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
15. 15.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) test on many groups (d) add more of the same
16. 16.🔍 Which is the odd one out? (a) test on one group only (b) add more of the same (c) skip testing (d) fix wrong labels
17. 17.🔍 Which is the odd one out? (a) balance the data (b) skip testing (c) test on one group only (d) copy old unfair choices
18. 18.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
19. 19.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
20. 20.🗣️ 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) children speak too fast (d) the phone was new
21. 21.Which of these helps reduce bias? (a) skip testing (b) ignore complaints (c) ask different people (d) add more of the same
22. 22.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
23. 23.True or false: “copy old unfair choices” helps reduce bias. (a) False (b) True
24. 24.🔍 Which is the odd one out? (a) use one group only (b) ask different people (c) check who is missing (d) test on many groups
25. 25.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
26. 26.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
27. 27.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
28. 28.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
29. 29.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
30. 30.True or false: “add more of the same” helps reduce bias. (a) True (b) False
31. 31.🔍 Which is the odd one out? (a) ask different people (b) skip testing (c) use one group only (d) test on one group only
32. 32.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
33. 33.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
34. 34.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
35. 35.True or false: “add more of the same” adds to bias. (a) False (b) True
36. 36.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
37. 37.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) bias from data (c) a broken screen (d) a fair model
38. 38.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
39. 39.Which of these adds to bias? (a) check who is missing (b) balance the data (c) add varied examples (d) skip testing
40. 40.🔍 Which is the odd one out? (a) test on many groups (b) ignore complaints (c) add more of the same (d) use one group only

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