AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 48 · 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.🖼️ 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
2. 2.🔍 Which is the odd one out? (a) use one group only (b) skip testing (c) copy old unfair choices (d) add varied examples
3. 3.Which of these adds to bias? (a) fix wrong labels (b) add varied examples (c) ask different people (d) test on one group only
4. 4.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
5. 5.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
6. 6.Which of these helps reduce bias? (a) copy old unfair choices (b) test on one group only (c) use one group only (d) balance the data
7. 7.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
8. 8.Which of these helps reduce bias? (a) fix wrong labels (b) ignore complaints (c) skip testing (d) test on one group only
9. 9.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
10. 10.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
11. 11.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
12. 12.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
13. 13.Which of these helps reduce bias? (a) test on one group only (b) check who is missing (c) ignore complaints (d) add more of the same
14. 14.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) add varied examples (d) test on one group only
15. 15.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
16. 16.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
17. 17.🔍 Which is the odd one out? (a) skip testing (b) check who is missing (c) use one group only (d) copy old unfair choices
18. 18.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
19. 19.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
20. 20.🛠️ What helps make an AI system fairer? (a) one group only (b) fewer tests (c) ignoring mistakes (d) more varied data
21. 21.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
22. 22.Which of these helps reduce bias? (a) skip testing (b) use one group only (c) test on many groups (d) add more of the same
23. 23.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
24. 24.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) children speak too fast (c) it was a rainy day (d) the phone was new
25. 25.🔍 Which is the odd one out? (a) ask different people (b) ignore complaints (c) fix wrong labels (d) test on many groups
26. 26.🔍 Which is the odd one out? (a) fix wrong labels (b) test on many groups (c) use one group only (d) balance the data
27. 27.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
28. 28.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
29. 29.Which of these adds to bias? (a) skip testing (b) add varied examples (c) check who is missing (d) test on many groups
30. 30.Which of these adds to bias? (a) test on many groups (b) check who is missing (c) use one group only (d) balance the data
31. 31.True or false: “check who is missing” helps reduce bias. (a) True (b) False
32. 32.Which of these adds to bias? (a) fix wrong labels (b) ignore complaints (c) balance the data (d) check who is missing
33. 33.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
34. 34.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
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.Which of these adds to bias? (a) check who is missing (b) ask different people (c) copy old unfair choices (d) fix wrong labels
37. 37.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
38. 38.True or false: “add varied examples” helps reduce bias. (a) True (b) False
39. 39.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
40. 40.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data

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