AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 873 · 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: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
2. 2.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) copy old unfair choices (d) ask different people
3. 3.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its training data (c) its colour (d) its screen size
4. 4.🔍 Which is the odd one out? (a) test on one group only (b) copy old unfair choices (c) ask different people (d) ignore complaints
5. 5.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
6. 6.Which of these adds to bias? (a) test on one group only (b) check who is missing (c) fix wrong labels (d) balance the data
7. 7.True or false: “test on many groups” helps reduce bias. (a) False (b) True
8. 8.🔍 Which is the odd one out? (a) ignore complaints (b) copy old unfair choices (c) use one group only (d) fix wrong labels
9. 9.Which of these helps reduce bias? (a) skip testing (b) use one group only (c) test on many groups (d) ignore complaints
10. 10.🗣️ 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) few child voices in data (d) the phone was new
11. 11.True or false: “add more of the same” adds to bias. (a) False (b) True
12. 12.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
13. 13.True or false: “add varied examples” adds to bias. (a) False (b) True
14. 14.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
15. 15.Which of these helps reduce bias? (a) add more of the same (b) add varied examples (c) test on one group only (d) skip testing
16. 16.🔍 Which is the odd one out? (a) test on one group only (b) test on many groups (c) copy old unfair choices (d) add more of the same
17. 17.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
18. 18.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
19. 19.True or false: “use one group only” helps reduce bias. (a) True (b) False
20. 20.🔍 Which is the odd one out? (a) copy old unfair choices (b) check who is missing (c) skip testing (d) use one group only
21. 21.Which of these helps reduce bias? (a) fix wrong labels (b) ignore complaints (c) use one group only (d) copy old unfair choices
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: “balance the data” helps reduce bias. (a) False (b) True
24. 24.🔍 Which is the odd one out? (a) test on many groups (b) balance the data (c) add varied examples (d) use one group only
25. 25.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
26. 26.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
27. 27.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
28. 28.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
29. 29.True or false: “test on one group only” adds to bias. (a) False (b) True
30. 30.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
31. 31.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) add more of the same (d) ask different people
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.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
34. 34.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
35. 35.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
36. 36.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
37. 37.Which of these adds to bias? (a) test on many groups (b) fix wrong labels (c) balance the data (d) skip testing
38. 38.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) bias from data (c) a strong battery (d) a broken screen
39. 39.🛠️ What helps make an AI system fairer? (a) one group only (b) fewer tests (c) ignoring mistakes (d) more varied data
40. 40.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

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