AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 222 · 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) add more of the same (b) skip testing (c) test on one group only (d) fix wrong labels
2. 2.🔍 Which is the odd one out? (a) use one group only (b) balance the data (c) copy old unfair choices (d) ignore complaints
3. 3.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its screen size (d) its training data
4. 4.True or false: “test on one group only” helps reduce bias. (a) True (b) False
5. 5.Which of these adds to bias? (a) ignore complaints (b) fix wrong labels (c) add varied examples (d) ask different people
6. 6.True or false: “ignore complaints” adds to bias. (a) False (b) True
7. 7.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
8. 8.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) ignoring mistakes (d) more varied data
9. 9.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
10. 10.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
11. 11.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a strong battery (c) a broken screen (d) bias from data
12. 12.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
13. 13.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
14. 14.🔍 Which is the odd one out? (a) check who is missing (b) use one group only (c) add varied examples (d) fix wrong labels
15. 15.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
16. 16.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
17. 17.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
18. 18.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
19. 19.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
20. 20.Which of these helps reduce bias? (a) ask different people (b) skip testing (c) ignore complaints (d) add more of the same
21. 21.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
22. 22.True or false: “ask different people” adds to bias. (a) True (b) False
23. 23.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
24. 24.🔍 Which is the odd one out? (a) balance the data (b) fix wrong labels (c) ask different people (d) skip testing
25. 25.🔍 Which is the odd one out? (a) skip testing (b) check who is missing (c) add more of the same (d) copy old unfair choices
26. 26.Which of these adds to bias? (a) check who is missing (b) ask different people (c) test on one group only (d) balance the data
27. 27.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
28. 28.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
29. 29.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
30. 30.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
31. 31.🔍 Which is the odd one out? (a) test on one group only (b) copy old unfair choices (c) test on many groups (d) ignore complaints
32. 32.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
33. 33.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
34. 34.🔍 Which is the odd one out? (a) test on many groups (b) add more of the same (c) check who is missing (d) ask different people
35. 35.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
36. 36.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
37. 37.True or false: “add varied examples” helps reduce bias. (a) True (b) False
38. 38.Which of these helps reduce bias? (a) test on one group only (b) copy old unfair choices (c) check who is missing (d) ignore complaints
39. 39.Which of these adds to bias? (a) add more of the same (b) test on many groups (c) add varied examples (d) fix wrong labels
40. 40.🔍 Which is the odd one out? (a) copy old unfair choices (b) use one group only (c) skip testing (d) ask different people

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