AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 874 · 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.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
2. 2.Which of these helps reduce bias? (a) use one group only (b) skip testing (c) test on one group only (d) ask different people
3. 3.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
4. 4.True or false: “add varied examples” adds to bias. (a) False (b) True
5. 5.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
6. 6.True or false: “test on many groups” adds to bias. (a) True (b) False
7. 7.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its screen size (c) its colour (d) the weather
8. 8.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
9. 9.🔍 Which is the odd one out? (a) fix wrong labels (b) ignore complaints (c) balance the data (d) check who is missing
10. 10.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a fair model (c) a broken screen (d) bias from data
11. 11.True or false: “skip testing” helps reduce bias. (a) False (b) True
12. 12.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
13. 13.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
14. 14.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
15. 15.Which of these adds to bias? (a) add varied examples (b) check who is missing (c) fix wrong labels (d) add more of the same
16. 16.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
17. 17.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
18. 18.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) True (b) False
19. 19.🔍 Which is the odd one out? (a) check who is missing (b) fix wrong labels (c) skip testing (d) ask different people
20. 20.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) ask different people (d) test on one group only
21. 21.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
22. 22.🔍 Which is the odd one out? (a) copy old unfair choices (b) skip testing (c) use one group only (d) ask different people
23. 23.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
24. 24.Which of these helps reduce bias? (a) skip testing (b) test on one group only (c) fix wrong labels (d) add more of the same
25. 25.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
26. 26.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
27. 27.Which of these helps reduce bias? (a) skip testing (b) test on many groups (c) test on one group only (d) add more of the same
28. 28.Which of these adds to bias? (a) test on one group only (b) test on many groups (c) ask different people (d) add varied examples
29. 29.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
30. 30.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
31. 31.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
32. 32.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
33. 33.True or false: “add varied examples” helps reduce bias. (a) True (b) False
34. 34.🔍 Which is the odd one out? (a) check who is missing (b) use one group only (c) balance the data (d) test on many groups
35. 35.🔍 Which is the odd one out? (a) balance the data (b) add varied examples (c) check who is missing (d) copy old unfair choices
36. 36.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
37. 37.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
38. 38.Which of these adds to bias? (a) test on many groups (b) skip testing (c) ask different people (d) check who is missing
39. 39.🔍 Which is the odd one out? (a) use one group only (b) add varied examples (c) test on one group only (d) add more of the same
40. 40.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) few child voices in data (c) the phone was new (d) children speak too fast

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