AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 513 · 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: stories where every doctor is a man? (a) one-sided data (b) balanced data
2. 2.🔍 Which is the odd one out? (a) fix wrong labels (b) skip testing (c) add varied examples (d) ask different people
3. 3.🔍 Which is the odd one out? (a) fix wrong labels (b) add more of the same (c) test on one group only (d) use one group only
4. 4.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) one group only (c) more varied data (d) fewer tests
5. 5.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
6. 6.True or false: “skip testing” adds to bias. (a) False (b) True
7. 7.🔍 Which is the odd one out? (a) use one group only (b) add varied examples (c) check who is missing (d) ask different people
8. 8.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
9. 9.True or false: “skip testing” helps reduce bias. (a) False (b) True
10. 10.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a strong battery (c) a fair model (d) a broken screen
11. 11.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
12. 12.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
13. 13.True or false: “test on many groups” adds to bias. (a) True (b) False
14. 14.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
15. 15.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
16. 16.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
17. 17.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
18. 18.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
19. 19.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
20. 20.Which of these adds to bias? (a) add more of the same (b) ask different people (c) add varied examples (d) fix wrong labels
21. 21.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) few child voices in data (c) it was a rainy day (d) the phone was new
22. 22.Which of these helps reduce bias? (a) add varied examples (b) ignore complaints (c) skip testing (d) test on one group only
23. 23.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
24. 24.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) ignore complaints (d) balance the data
25. 25.🔍 Which is the odd one out? (a) ignore complaints (b) copy old unfair choices (c) use one group only (d) balance the data
26. 26.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
27. 27.Which of these adds to bias? (a) balance the data (b) test on one group only (c) test on many groups (d) ask different people
28. 28.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
29. 29.Which of these helps reduce bias? (a) add more of the same (b) fix wrong labels (c) copy old unfair choices (d) test on one group only
30. 30.🔍 Which is the odd one out? (a) balance the data (b) ask different people (c) add varied examples (d) copy old unfair choices
31. 31.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
32. 32.Which of these helps reduce bias? (a) skip testing (b) ignore complaints (c) ask different people (d) add more of the same
33. 33.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
34. 34.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
35. 35.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
36. 36.True or false: “check who is missing” helps reduce bias. (a) False (b) True
37. 37.True or false: “test on one group only” helps reduce bias. (a) True (b) False
38. 38.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
39. 39.True or false: “balance the data” helps reduce bias. (a) True (b) False
40. 40.🔍 Which is the odd one out? (a) add varied examples (b) test on one group only (c) check who is missing (d) test on many groups

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