AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 734 · 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.🛠️ What helps make an AI system fairer? (a) one group only (b) ignoring mistakes (c) more varied data (d) fewer tests
2. 2.True or false: “fix wrong labels” adds to bias. (a) True (b) False
3. 3.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
4. 4.🔍 Which is the odd one out? (a) copy old unfair choices (b) ignore complaints (c) check who is missing (d) skip testing
5. 5.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its colour (c) its screen size (d) the weather
6. 6.Which of these helps reduce bias? (a) balance the data (b) copy old unfair choices (c) test on one group only (d) skip testing
7. 7.True or false: “check who is missing” adds to bias. (a) False (b) True
8. 8.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
9. 9.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
10. 10.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
11. 11.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) check who is missing (d) add more of the same
12. 12.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
13. 13.True or false: “balance the data” helps reduce bias. (a) False (b) True
14. 14.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
15. 15.Which of these adds to bias? (a) add varied examples (b) test on many groups (c) ask different people (d) test on one group only
16. 16.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
17. 17.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
18. 18.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a strong battery (c) a fair model (d) a broken screen
19. 19.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
20. 20.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
21. 21.🔍 Which is the odd one out? (a) ask different people (b) add varied examples (c) fix wrong labels (d) ignore complaints
22. 22.True or false: “add more of the same” helps reduce bias. (a) False (b) True
23. 23.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
24. 24.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
25. 25.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
26. 26.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
27. 27.🔍 Which is the odd one out? (a) ask different people (b) check who is missing (c) add varied examples (d) copy old unfair choices
28. 28.Which of these helps reduce bias? (a) test on many groups (b) add more of the same (c) test on one group only (d) skip testing
29. 29.🔍 Which is the odd one out? (a) balance the data (b) add more of the same (c) check who is missing (d) test on many groups
30. 30.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
31. 31.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
32. 32.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
33. 33.True or false: “add varied examples” helps reduce bias. (a) False (b) True
34. 34.🗣️ 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
35. 35.Which of these helps reduce bias? (a) test on one group only (b) skip testing (c) copy old unfair choices (d) fix wrong labels
36. 36.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
37. 37.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
38. 38.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
39. 39.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
40. 40.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data

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