AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 374 · 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.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its screen size (c) the weather (d) its colour
2. 2.🔍 Which is the odd one out? (a) check who is missing (b) test on many groups (c) ask different people (d) add more of the same
3. 3.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
4. 4.🔍 Which is the odd one out? (a) copy old unfair choices (b) test on many groups (c) ignore complaints (d) test on one group only
5. 5.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
6. 6.Which of these adds to bias? (a) test on many groups (b) add varied examples (c) check who is missing (d) skip testing
7. 7.Which of these helps reduce bias? (a) add more of the same (b) add varied examples (c) skip testing (d) copy old unfair choices
8. 8.🔍 Which is the odd one out? (a) check who is missing (b) fix wrong labels (c) add varied examples (d) skip testing
9. 9.🔍 Which is the odd one out? (a) copy old unfair choices (b) skip testing (c) ask different people (d) ignore complaints
10. 10.Which of these adds to bias? (a) test on one group only (b) test on many groups (c) check who is missing (d) add varied examples
11. 11.🗣️ 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
12. 12.🔍 Which is the odd one out? (a) use one group only (b) add more of the same (c) skip testing (d) add varied examples
13. 13.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
14. 14.🔍 Which is the odd one out? (a) check who is missing (b) fix wrong labels (c) add varied examples (d) test on one group only
15. 15.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
16. 16.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) more varied data (d) ignoring mistakes
17. 17.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
18. 18.🔍 Which is the odd one out? (a) use one group only (b) fix wrong labels (c) copy old unfair choices (d) test on one group only
19. 19.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
20. 20.Which of these helps reduce bias? (a) use one group only (b) ask different people (c) copy old unfair choices (d) test on one group only
21. 21.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) False (b) True
22. 22.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
23. 23.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
24. 24.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) False (b) True
25. 25.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
26. 26.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
27. 27.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
28. 28.True or false: “skip testing” adds to bias. (a) True (b) False
29. 29.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
30. 30.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
31. 31.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
32. 32.True or false: “add more of the same” helps reduce bias. (a) True (b) False
33. 33.True or false: “skip testing” helps reduce bias. (a) True (b) False
34. 34.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
35. 35.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
36. 36.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
37. 37.Which of these adds to bias? (a) fix wrong labels (b) use one group only (c) check who is missing (d) add varied examples
38. 38.True or false: “test on many groups” adds to bias. (a) False (b) True
39. 39.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
40. 40.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False

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