AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 326 · 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.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) it was a rainy day (c) children speak too fast (d) few child voices in data
2. 2.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
3. 3.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
4. 4.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a broken screen (c) bias from data (d) a fair model
5. 5.Which of these helps reduce bias? (a) use one group only (b) add varied examples (c) ignore complaints (d) test on one group only
6. 6.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
7. 7.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
8. 8.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
9. 9.🔍 Which is the odd one out? (a) use one group only (b) copy old unfair choices (c) ask different people (d) skip testing
10. 10.🔍 Which is the odd one out? (a) skip testing (b) add more of the same (c) copy old unfair choices (d) fix wrong labels
11. 11.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) the weather (c) its colour (d) its screen size
12. 12.True or false: “check who is missing” adds to bias. (a) False (b) True
13. 13.🔍 Which is the odd one out? (a) skip testing (b) test on one group only (c) add more of the same (d) add varied examples
14. 14.🔍 Which is the odd one out? (a) fix wrong labels (b) ignore complaints (c) ask different people (d) check who is missing
15. 15.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
16. 16.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
17. 17.🔍 Which is the odd one out? (a) test on many groups (b) use one group only (c) skip testing (d) test on one group only
18. 18.True or false: “ignore complaints” adds to bias. (a) True (b) False
19. 19.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
20. 20.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
21. 21.True or false: “test on many groups” adds to bias. (a) False (b) True
22. 22.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
23. 23.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
24. 24.🔍 Which is the odd one out? (a) test on many groups (b) fix wrong labels (c) balance the data (d) use one group only
25. 25.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
26. 26.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
27. 27.True or false: “ask different people” adds to bias. (a) False (b) True
28. 28.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
29. 29.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
30. 30.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
31. 31.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
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.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
34. 34.🛠️ What helps make an AI system fairer? (a) fewer tests (b) ignoring mistakes (c) more varied data (d) one group only
35. 35.🔍 Which is the odd one out? (a) add more of the same (b) copy old unfair choices (c) balance the data (d) ignore complaints
36. 36.True or false: “test on one group only” adds to bias. (a) False (b) True
37. 37.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
38. 38.🔍 Which is the odd one out? (a) add more of the same (b) test on one group only (c) ignore complaints (d) check who is missing
39. 39.🔍 Which is the odd one out? (a) copy old unfair choices (b) test on many groups (c) add varied examples (d) balance the data
40. 40.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

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