AI Bias Worksheet for Kids

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

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