AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 132 · 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: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
2. 2.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
3. 3.Which of these helps reduce bias? (a) add more of the same (b) ignore complaints (c) fix wrong labels (d) copy old unfair choices
4. 4.🛠️ What helps make an AI system fairer? (a) one group only (b) ignoring mistakes (c) fewer tests (d) more varied data
5. 5.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
6. 6.🖼️ 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
7. 7.True or false: “skip testing” adds to bias. (a) False (b) True
8. 8.Which of these helps reduce bias? (a) copy old unfair choices (b) test on many groups (c) use one group only (d) ignore complaints
9. 9.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
10. 10.Which of these adds to bias? (a) balance the data (b) ask different people (c) test on many groups (d) skip testing
11. 11.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
12. 12.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) it was a rainy day (c) few child voices in data (d) children speak too fast
13. 13.True or false: “balance the data” helps reduce bias. (a) False (b) True
14. 14.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) False (b) True
15. 15.🛠️ Does this reduce bias or add to it: ignore complaints? (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.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
18. 18.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
19. 19.🔍 Which is the odd one out? (a) test on one group only (b) check who is missing (c) balance the data (d) test on many groups
20. 20.🔍 Which is the odd one out? (a) balance the data (b) skip testing (c) add more of the same (d) copy old unfair choices
21. 21.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
22. 22.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
23. 23.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
24. 24.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
25. 25.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
26. 26.Which of these adds to bias? (a) ignore complaints (b) balance the data (c) add varied examples (d) test on many groups
27. 27.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
28. 28.True or false: “test on one group only” helps reduce bias. (a) True (b) False
29. 29.Which of these helps reduce bias? (a) ignore complaints (b) check who is missing (c) use one group only (d) add more of the same
30. 30.🔍 Which is the odd one out? (a) ask different people (b) ignore complaints (c) copy old unfair choices (d) use one group only
31. 31.True or false: “test on many groups” adds to bias. (a) True (b) False
32. 32.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
33. 33.Which of these adds to bias? (a) test on many groups (b) check who is missing (c) add more of the same (d) add varied examples
34. 34.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
35. 35.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
36. 36.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
37. 37.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
38. 38.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
39. 39.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
40. 40.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias

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