AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 991 · 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: voices of children and adults from many regions? (a) one-sided data (b) balanced data
2. 2.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
3. 3.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) children speak too fast (c) few child voices in data (d) it was a rainy day
4. 4.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) the weather (c) its colour (d) its training data
5. 5.🔍 Which is the odd one out? (a) add more of the same (b) balance the data (c) test on many groups (d) check who is missing
6. 6.Which of these helps reduce bias? (a) test on one group only (b) ignore complaints (c) check who is missing (d) use one group only
7. 7.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
8. 8.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
9. 9.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
10. 10.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) one group only (c) fewer tests (d) more varied data
11. 11.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
12. 12.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
13. 13.🔍 Which is the odd one out? (a) test on one group only (b) test on many groups (c) add more of the same (d) copy old unfair choices
14. 14.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
15. 15.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
16. 16.True or false: “use one group only” helps reduce bias. (a) True (b) False
17. 17.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
18. 18.True or false: “use one group only” adds to bias. (a) True (b) False
19. 19.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
20. 20.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
21. 21.🔍 Which is the odd one out? (a) test on one group only (b) skip testing (c) copy old unfair choices (d) ask different people
22. 22.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
23. 23.True or false: “test on many groups” helps reduce bias. (a) False (b) True
24. 24.Which of these helps reduce bias? (a) ignore complaints (b) add varied examples (c) add more of the same (d) skip testing
25. 25.🔍 Which is the odd one out? (a) test on one group only (b) skip testing (c) balance the data (d) copy old unfair choices
26. 26.🔍 Which is the odd one out? (a) copy old unfair choices (b) balance the data (c) ask different people (d) fix wrong labels
27. 27.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a strong battery (c) a broken screen (d) a fair model
28. 28.True or false: “add varied examples” adds to bias. (a) True (b) False
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: fix wrong labels? (a) reduces bias (b) adds to bias
31. 31.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
32. 32.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
33. 33.Which of these adds to bias? (a) test on many groups (b) balance the data (c) fix wrong labels (d) ignore complaints
34. 34.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) False (b) True
35. 35.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
36. 36.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
37. 37.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
38. 38.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
39. 39.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
40. 40.Which of these adds to bias? (a) fix wrong labels (b) ask different people (c) add more of the same (d) add varied examples

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