AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 986 · 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.🗣️ 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) children speak too fast (d) it was a rainy day
2. 2.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
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: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
5. 5.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
6. 6.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
7. 7.🔍 Which is the odd one out? (a) copy old unfair choices (b) test on many groups (c) use one group only (d) add more of the same
8. 8.🔍 Which is the odd one out? (a) balance the data (b) ignore complaints (c) check who is missing (d) add varied examples
9. 9.Which of these adds to bias? (a) check who is missing (b) fix wrong labels (c) ask different people (d) ignore complaints
10. 10.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
11. 11.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) the weather (c) its screen size (d) its colour
12. 12.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
13. 13.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
14. 14.🔍 Which is the odd one out? (a) balance the data (b) add varied examples (c) copy old unfair choices (d) fix wrong labels
15. 15.🔍 Which is the odd one out? (a) test on one group only (b) skip testing (c) add varied examples (d) add more of the same
16. 16.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
17. 17.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
18. 18.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
19. 19.Which of these helps reduce bias? (a) copy old unfair choices (b) ask different people (c) ignore complaints (d) skip testing
20. 20.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
21. 21.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
22. 22.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
23. 23.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
24. 24.🔍 Which is the odd one out? (a) copy old unfair choices (b) ask different people (c) add more of the same (d) use one group only
25. 25.🔍 Which is the odd one out? (a) ask different people (b) add varied examples (c) skip testing (d) fix wrong labels
26. 26.Which of these helps reduce bias? (a) test on one group only (b) skip testing (c) add more of the same (d) add varied examples
27. 27.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
28. 28.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
29. 29.🔍 Which is the odd one out? (a) fix wrong labels (b) ignore complaints (c) test on one group only (d) skip testing
30. 30.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
31. 31.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
32. 32.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
33. 33.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
34. 34.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
35. 35.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
36. 36.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
37. 37.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
38. 38.🛠️ What helps make an AI system fairer? (a) more varied data (b) one group only (c) ignoring mistakes (d) fewer tests
39. 39.True or false: “test on one group only” helps reduce bias. (a) True (b) False
40. 40.🔍 Which is the odd one out? (a) add varied examples (b) balance the data (c) check who is missing (d) test on one group only

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