AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 811 · 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.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
2. 2.🔍 Which is the odd one out? (a) test on many groups (b) fix wrong labels (c) add varied examples (d) use one group only
3. 3.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
4. 4.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
5. 5.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) the phone was new (c) few child voices in data (d) children speak too fast
6. 6.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
7. 7.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
8. 8.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
9. 9.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
10. 10.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) bias from data (c) a broken screen (d) a strong battery
11. 11.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
12. 12.🔍 Which is the odd one out? (a) use one group only (b) ignore complaints (c) copy old unfair choices (d) balance the data
13. 13.Which of these adds to bias? (a) balance the data (b) copy old unfair choices (c) add varied examples (d) fix wrong labels
14. 14.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
15. 15.Which of these adds to bias? (a) check who is missing (b) skip testing (c) test on many groups (d) ask different people
16. 16.Which of these helps reduce bias? (a) ignore complaints (b) test on one group only (c) fix wrong labels (d) use one group only
17. 17.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
18. 18.True or false: “skip testing” helps reduce bias. (a) True (b) False
19. 19.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
20. 20.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
21. 21.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
22. 22.🔍 Which is the odd one out? (a) balance the data (b) test on one group only (c) check who is missing (d) add varied examples
23. 23.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
24. 24.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
25. 25.Which of these adds to bias? (a) test on one group only (b) ask different people (c) add varied examples (d) check who is missing
26. 26.Which of these adds to bias? (a) add varied examples (b) use one group only (c) ask different people (d) fix wrong labels
27. 27.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
28. 28.🛠️ What helps make an AI system fairer? (a) one group only (b) ignoring mistakes (c) fewer tests (d) more varied data
29. 29.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
30. 30.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) True (b) False
31. 31.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
32. 32.🔍 Which is the odd one out? (a) test on many groups (b) copy old unfair choices (c) add varied examples (d) balance the data
33. 33.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
34. 34.🔍 Which is the odd one out? (a) add more of the same (b) copy old unfair choices (c) use one group only (d) add varied examples
35. 35.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
36. 36.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
37. 37.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
38. 38.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
39. 39.Which of these helps reduce bias? (a) skip testing (b) test on many groups (c) add more of the same (d) test on one group only
40. 40.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True

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