AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 351 · 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) fewer tests (b) more varied data (c) one group only (d) ignoring mistakes
2. 2.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
3. 3.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
4. 4.Which of these adds to bias? (a) fix wrong labels (b) balance the data (c) add more of the same (d) add varied examples
5. 5.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) its colour (d) the weather
6. 6.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
7. 7.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
8. 8.True or false: “balance the data” helps reduce bias. (a) False (b) True
9. 9.True or false: “test on many groups” helps reduce bias. (a) False (b) True
10. 10.True or false: “fix wrong labels” adds to bias. (a) True (b) False
11. 11.True or false: “test on many groups” adds to bias. (a) True (b) False
12. 12.Which of these helps reduce bias? (a) use one group only (b) ignore complaints (c) check who is missing (d) skip testing
13. 13.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
14. 14.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
15. 15.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) bias from data (c) a fair model (d) a strong battery
16. 16.Which of these adds to bias? (a) fix wrong labels (b) add varied examples (c) check who is missing (d) test on one group only
17. 17.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
18. 18.🔍 Which is the odd one out? (a) skip testing (b) ask different people (c) copy old unfair choices (d) use one group only
19. 19.Which of these helps reduce bias? (a) ignore complaints (b) test on one group only (c) test on many groups (d) use one group only
20. 20.🔍 Which is the odd one out? (a) fix wrong labels (b) balance the data (c) add varied examples (d) use one group only
21. 21.🔍 Which is the odd one out? (a) check who is missing (b) test on one group only (c) test on many groups (d) add varied examples
22. 22.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
23. 23.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
24. 24.True or false: “copy old unfair choices” adds to bias. (a) False (b) True
25. 25.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
26. 26.🗣️ 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
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.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
29. 29.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
30. 30.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
31. 31.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
32. 32.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
33. 33.🔍 Which is the odd one out? (a) skip testing (b) fix wrong labels (c) test on one group only (d) use one group only
34. 34.Which of these adds to bias? (a) add varied examples (b) check who is missing (c) fix wrong labels (d) ignore complaints
35. 35.Which of these helps reduce bias? (a) test on one group only (b) fix wrong labels (c) use one group only (d) copy old unfair choices
36. 36.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
37. 37.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
38. 38.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
39. 39.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
40. 40.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data

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