AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 679 · 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 voice app trained only on adult voices? (a) balanced data (b) one-sided data
2. 2.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
3. 3.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) bias from data (c) a broken screen (d) a fair model
4. 4.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
5. 5.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
6. 6.True or false: “skip testing” helps reduce bias. (a) True (b) False
7. 7.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
8. 8.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) the weather (c) its colour (d) its screen size
9. 9.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
10. 10.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
11. 11.🛠️ What helps make an AI system fairer? (a) fewer tests (b) ignoring mistakes (c) one group only (d) more varied data
12. 12.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
13. 13.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
14. 14.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
15. 15.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
16. 16.Which of these adds to bias? (a) balance the data (b) ask different people (c) test on many groups (d) test on one group only
17. 17.🔍 Which is the odd one out? (a) balance the data (b) use one group only (c) add varied examples (d) test on many groups
18. 18.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
19. 19.Which of these helps reduce bias? (a) test on one group only (b) ignore complaints (c) fix wrong labels (d) add more of the same
20. 20.🗣️ 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
21. 21.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
22. 22.True or false: “test on one group only” helps reduce bias. (a) True (b) False
23. 23.Which of these adds to bias? (a) copy old unfair choices (b) fix wrong labels (c) balance the data (d) check who is missing
24. 24.🔍 Which is the odd one out? (a) test on one group only (b) test on many groups (c) copy old unfair choices (d) use one group only
25. 25.True or false: “check who is missing” adds to bias. (a) False (b) True
26. 26.True or false: “test on one group only” adds to bias. (a) True (b) False
27. 27.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
28. 28.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
29. 29.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
30. 30.True or false: “check who is missing” helps reduce bias. (a) False (b) True
31. 31.🔍 Which is the odd one out? (a) test on one group only (b) balance the data (c) fix wrong labels (d) check who is missing
32. 32.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
33. 33.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
34. 34.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
35. 35.Which of these helps reduce bias? (a) balance the data (b) copy old unfair choices (c) use one group only (d) test on one group only
36. 36.Which of these helps reduce bias? (a) ask different people (b) test on one group only (c) use one group only (d) copy old unfair choices
37. 37.🔍 Which is the odd one out? (a) add varied examples (b) test on one group only (c) copy old unfair choices (d) use one group only
38. 38.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
39. 39.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
40. 40.Which of these helps reduce bias? (a) skip testing (b) add more of the same (c) test on many groups (d) ignore complaints

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