AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 124 · 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: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
2. 2.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
3. 3.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
4. 4.🔍 Which is the odd one out? (a) skip testing (b) add more of the same (c) test on one group only (d) fix wrong labels
5. 5.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
6. 6.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
7. 7.Which of these adds to bias? (a) test on many groups (b) balance the data (c) add varied examples (d) test on one group only
8. 8.Which of these adds to bias? (a) skip testing (b) test on many groups (c) fix wrong labels (d) ask different people
9. 9.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
10. 10.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
11. 11.🗣️ 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) the phone was new (d) few child voices in data
12. 12.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
13. 13.Which of these adds to bias? (a) ask different people (b) use one group only (c) test on many groups (d) check who is missing
14. 14.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
15. 15.Which of these adds to bias? (a) fix wrong labels (b) add varied examples (c) copy old unfair choices (d) test on many groups
16. 16.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
17. 17.🔍 Which is the odd one out? (a) balance the data (b) skip testing (c) check who is missing (d) test on many groups
18. 18.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
19. 19.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
20. 20.Which of these helps reduce bias? (a) fix wrong labels (b) test on one group only (c) skip testing (d) copy old unfair choices
21. 21.True or false: “ask different people” adds to bias. (a) False (b) True
22. 22.🔍 Which is the odd one out? (a) ask different people (b) copy old unfair choices (c) ignore complaints (d) skip testing
23. 23.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a fair model (c) bias from data (d) a broken screen
24. 24.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) check who is missing (d) add more of the same
25. 25.True or false: “ignore complaints” adds to bias. (a) False (b) True
26. 26.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
27. 27.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
28. 28.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its screen size (d) its training data
29. 29.True or false: “balance the data” adds to bias. (a) False (b) True
30. 30.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) True (b) False
31. 31.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
32. 32.🔍 Which is the odd one out? (a) balance the data (b) add varied examples (c) copy old unfair choices (d) ask different people
33. 33.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
34. 34.True or false: “add more of the same” adds to bias. (a) True (b) False
35. 35.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
36. 36.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
37. 37.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
38. 38.True or false: “check who is missing” helps reduce bias. (a) False (b) True
39. 39.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
40. 40.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True

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