AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 238 · 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.🔍 Which is the odd one out? (a) fix wrong labels (b) balance the data (c) ignore complaints (d) check who is missing
2. 2.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
3. 3.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
4. 4.True or false: “add varied examples” adds to bias. (a) True (b) False
5. 5.🔍 Which is the odd one out? (a) add varied examples (b) balance the data (c) test on one group only (d) test on many groups
6. 6.Which of these helps reduce bias? (a) copy old unfair choices (b) test on one group only (c) ignore complaints (d) test on many groups
7. 7.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
8. 8.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
9. 9.🔍 Which is the odd one out? (a) add more of the same (b) check who is missing (c) ask different people (d) test on many groups
10. 10.Which of these helps reduce bias? (a) copy old unfair choices (b) add varied examples (c) ignore complaints (d) use one group only
11. 11.🗣️ 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
12. 12.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
13. 13.True or false: “use one group only” adds to bias. (a) True (b) False
14. 14.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
15. 15.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
16. 16.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
17. 17.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
18. 18.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
19. 19.🔍 Which is the odd one out? (a) add varied examples (b) skip testing (c) fix wrong labels (d) check who is missing
20. 20.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
21. 21.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
22. 22.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
23. 23.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
24. 24.🔍 Which is the odd one out? (a) check who is missing (b) add varied examples (c) fix wrong labels (d) copy old unfair choices
25. 25.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
26. 26.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a fair model (c) bias from data (d) a strong battery
27. 27.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
28. 28.🔍 Which is the odd one out? (a) use one group only (b) check who is missing (c) add more of the same (d) copy old unfair choices
29. 29.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) fix wrong labels (d) test on one group only
30. 30.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
31. 31.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
32. 32.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) False (b) True
33. 33.🔍 Which is the odd one out? (a) test on one group only (b) add varied examples (c) ignore complaints (d) use one group only
34. 34.🛠️ What helps make an AI system fairer? (a) one group only (b) fewer tests (c) ignoring mistakes (d) more varied data
35. 35.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
36. 36.🔍 Which is the odd one out? (a) ignore complaints (b) ask different people (c) skip testing (d) copy old unfair choices
37. 37.🔍 Which is the odd one out? (a) balance the data (b) test on many groups (c) add varied examples (d) use one group only
38. 38.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its screen size (c) its colour (d) its training data
39. 39.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
40. 40.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias

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