AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 344 · 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: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
2. 2.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
3. 3.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
4. 4.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) the weather (c) its screen size (d) its training data
5. 5.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
6. 6.True or false: “test on many groups” adds to bias. (a) True (b) False
7. 7.🔍 Which is the odd one out? (a) test on many groups (b) check who is missing (c) fix wrong labels (d) ignore complaints
8. 8.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
9. 9.🔍 Which is the odd one out? (a) skip testing (b) add varied examples (c) copy old unfair choices (d) test on one group only
10. 10.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
11. 11.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a strong battery (c) bias from data (d) a fair model
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.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
14. 14.True or false: “add more of the same” adds to bias. (a) False (b) True
15. 15.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
16. 16.Which of these adds to bias? (a) test on many groups (b) fix wrong labels (c) use one group only (d) balance the data
17. 17.🔍 Which is the odd one out? (a) ask different people (b) add more of the same (c) use one group only (d) test on one group only
18. 18.Which of these adds to bias? (a) check who is missing (b) add varied examples (c) ignore complaints (d) test on many groups
19. 19.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
20. 20.🛠️ What helps make an AI system fairer? (a) one group only (b) fewer tests (c) more varied data (d) ignoring mistakes
21. 21.True or false: “fix wrong labels” adds to bias. (a) False (b) True
22. 22.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False
23. 23.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
24. 24.🔍 Which is the odd one out? (a) balance the data (b) add more of the same (c) skip testing (d) ignore complaints
25. 25.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
26. 26.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
27. 27.🔍 Which is the odd one out? (a) add more of the same (b) fix wrong labels (c) test on many groups (d) add varied examples
28. 28.Which of these adds to bias? (a) balance the data (b) test on one group only (c) add varied examples (d) ask different people
29. 29.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
30. 30.Which of these helps reduce bias? (a) ignore complaints (b) use one group only (c) fix wrong labels (d) copy old unfair choices
31. 31.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
32. 32.🗣️ 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
33. 33.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
34. 34.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) copy old unfair choices (d) balance the data
35. 35.True or false: “ask different people” helps reduce bias. (a) True (b) False
36. 36.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
37. 37.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
38. 38.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
39. 39.Which of these helps reduce bias? (a) test on one group only (b) add more of the same (c) ignore complaints (d) check who is missing
40. 40.True or false: “skip testing” adds to bias. (a) True (b) False

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