AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 100 · 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: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
2. 2.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
3. 3.True or false: “check who is missing” helps reduce bias. (a) True (b) False
4. 4.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
5. 5.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) one group only (c) fewer tests (d) more varied data
6. 6.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
7. 7.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
8. 8.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
9. 9.Which of these helps reduce bias? (a) ignore complaints (b) copy old unfair choices (c) test on many groups (d) skip testing
10. 10.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
11. 11.🔍 Which is the odd one out? (a) add varied examples (b) balance the data (c) test on one group only (d) ask different people
12. 12.True or false: “add varied examples” adds to bias. (a) False (b) True
13. 13.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
14. 14.🔍 Which is the odd one out? (a) test on one group only (b) add varied examples (c) ignore complaints (d) add more of the same
15. 15.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
16. 16.Which of these adds to bias? (a) test on many groups (b) use one group only (c) ask different people (d) add varied examples
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.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its training data (d) its screen size
19. 19.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
20. 20.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
21. 21.True or false: “check who is missing” adds to bias. (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) True (b) False
23. 23.🔍 Which is the odd one out? (a) test on one group only (b) use one group only (c) fix wrong labels (d) add more of the same
24. 24.True or false: “test on many groups” helps reduce bias. (a) True (b) False
25. 25.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
26. 26.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a strong battery (c) bias from data (d) a fair model
27. 27.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
28. 28.Which of these helps reduce bias? (a) check who is missing (b) ignore complaints (c) test on one group only (d) use one group only
29. 29.🗣️ 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) children speak too fast (d) few child voices in data
30. 30.🔍 Which is the odd one out? (a) fix wrong labels (b) test on many groups (c) add more of the same (d) add varied examples
31. 31.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
32. 32.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
33. 33.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
34. 34.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
35. 35.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
36. 36.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
37. 37.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
38. 38.Which of these adds to bias? (a) fix wrong labels (b) test on many groups (c) balance the data (d) ignore complaints
39. 39.🔍 Which is the odd one out? (a) ask different people (b) add varied examples (c) check who is missing (d) ignore complaints
40. 40.True or false: “ask different people” helps reduce bias. (a) True (b) False

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