AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 311 · 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.True or false: “check who is missing” helps reduce bias. (a) False (b) True
2. 2.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
3. 3.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
4. 4.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
5. 5.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) the weather (d) its colour
6. 6.🔍 Which is the odd one out? (a) test on many groups (b) use one group only (c) fix wrong labels (d) check who is missing
7. 7.🔍 Which is the odd one out? (a) use one group only (b) ignore complaints (c) add more of the same (d) fix wrong labels
8. 8.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
9. 9.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
10. 10.🖼️ 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
11. 11.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
12. 12.True or false: neat and messy writing from many people 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 many groups (b) ignore complaints (c) add more of the same (d) skip testing
14. 14.Which of these adds to bias? (a) ask different people (b) add more of the same (c) test on many groups (d) add varied examples
15. 15.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
16. 16.True or false: “skip testing” helps reduce bias. (a) True (b) False
17. 17.True or false: “test on many groups” adds to bias. (a) True (b) False
18. 18.🔍 Which is the odd one out? (a) copy old unfair choices (b) ask different people (c) ignore complaints (d) skip testing
19. 19.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) the phone was new (c) it was a rainy day (d) few child voices in data
20. 20.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
21. 21.🔍 Which is the odd one out? (a) add varied examples (b) skip testing (c) test on many groups (d) ask different people
22. 22.True or false: “ask different people” adds to bias. (a) True (b) False
23. 23.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
24. 24.Which of these adds to bias? (a) ignore complaints (b) ask different people (c) add varied examples (d) test on many groups
25. 25.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
26. 26.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
27. 27.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
28. 28.Which of these helps reduce bias? (a) copy old unfair choices (b) use one group only (c) balance the data (d) test on one group only
29. 29.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
30. 30.Which of these adds to bias? (a) fix wrong labels (b) ask different people (c) add varied examples (d) skip testing
31. 31.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
32. 32.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
33. 33.Which of these adds to bias? (a) balance the data (b) copy old unfair choices (c) check who is missing (d) fix wrong labels
34. 34.True or false: “add more of the same” adds to bias. (a) False (b) True
35. 35.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
36. 36.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
37. 37.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
38. 38.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
39. 39.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
40. 40.True or false: “ask different people” helps reduce bias. (a) False (b) True

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