AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 680 · 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.🔍 Which is the odd one out? (a) test on many groups (b) fix wrong labels (c) skip testing (d) balance the data
2. 2.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its training data (c) its colour (d) its screen size
3. 3.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
4. 4.Which of these adds to bias? (a) test on many groups (b) fix wrong labels (c) test on one group only (d) add varied examples
5. 5.🔍 Which is the odd one out? (a) test on one group only (b) fix wrong labels (c) check who is missing (d) test on many groups
6. 6.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
7. 7.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
8. 8.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) more varied data (c) fewer tests (d) one group only
9. 9.True or false: “test on one group only” adds to bias. (a) True (b) False
10. 10.🖼️ 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
11. 11.True or false: “use one group only” adds to bias. (a) False (b) True
12. 12.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
13. 13.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
14. 14.Which of these adds to bias? (a) add varied examples (b) use one group only (c) test on many groups (d) fix wrong labels
15. 15.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
16. 16.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) it was a rainy day (c) the phone was new (d) few child voices in data
17. 17.Which of these helps reduce bias? (a) add more of the same (b) test on one group only (c) copy old unfair choices (d) balance the data
18. 18.True or false: “test on many groups” adds to bias. (a) True (b) False
19. 19.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
20. 20.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
21. 21.True or false: “ignore complaints” adds to bias. (a) True (b) False
22. 22.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
23. 23.Which of these adds to bias? (a) balance the data (b) ask different people (c) copy old unfair choices (d) add varied examples
24. 24.Which of these helps reduce bias? (a) test on one group only (b) fix wrong labels (c) skip testing (d) copy old unfair choices
25. 25.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
26. 26.Which of these adds to bias? (a) add varied examples (b) ignore complaints (c) ask different people (d) test on many groups
27. 27.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
28. 28.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
29. 29.True or false: “add more of the same” adds to bias. (a) False (b) True
30. 30.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
31. 31.Which of these helps reduce bias? (a) use one group only (b) copy old unfair choices (c) check who is missing (d) skip testing
32. 32.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
33. 33.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
34. 34.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
35. 35.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
36. 36.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
37. 37.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
38. 38.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
39. 39.🔍 Which is the odd one out? (a) skip testing (b) fix wrong labels (c) test on one group only (d) ignore complaints
40. 40.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data

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