AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 240 · 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) copy old unfair choices (b) add more of the same (c) add varied examples (d) ignore complaints
2. 2.True or false: “add varied examples” adds to bias. (a) True (b) False
3. 3.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
4. 4.🔍 Which is the odd one out? (a) test on many groups (b) add varied examples (c) copy old unfair choices (d) ask different people
5. 5.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
6. 6.🔍 Which is the odd one out? (a) copy old unfair choices (b) add more of the same (c) ignore complaints (d) check who is missing
7. 7.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
8. 8.Which of these helps reduce bias? (a) copy old unfair choices (b) use one group only (c) check who is missing (d) ignore complaints
9. 9.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
10. 10.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
11. 11.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) the weather (d) its training data
12. 12.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
13. 13.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
14. 14.Which of these helps reduce bias? (a) ignore complaints (b) balance the data (c) add more of the same (d) use one group only
15. 15.True or false: “ignore complaints” adds to bias. (a) False (b) True
16. 16.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
17. 17.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
18. 18.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
19. 19.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) check who is missing (d) add more of the same
20. 20.Which of these adds to bias? (a) ask different people (b) balance the data (c) check who is missing (d) copy old unfair choices
21. 21.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
22. 22.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
23. 23.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
24. 24.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) bias from data (c) a strong battery (d) a broken screen
25. 25.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
26. 26.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
27. 27.Which of these helps reduce bias? (a) use one group only (b) add more of the same (c) ignore complaints (d) fix wrong labels
28. 28.Which of these helps reduce bias? (a) skip testing (b) ignore complaints (c) add varied examples (d) use one group only
29. 29.🔍 Which is the odd one out? (a) test on many groups (b) balance the data (c) check who is missing (d) use one group only
30. 30.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
31. 31.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) the phone was new (c) it was a rainy day (d) few child voices in data
32. 32.True or false: “test on many groups” helps reduce bias. (a) False (b) True
33. 33.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
34. 34.🔍 Which is the odd one out? (a) balance the data (b) ignore complaints (c) add varied examples (d) check who is missing
35. 35.🔍 Which is the odd one out? (a) test on one group only (b) test on many groups (c) add more of the same (d) use one group only
36. 36.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
37. 37.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
38. 38.Which of these adds to bias? (a) balance the data (b) fix wrong labels (c) ask different people (d) use one group only
39. 39.🛠️ What helps make an AI system fairer? (a) fewer tests (b) more varied data (c) ignoring mistakes (d) one group only
40. 40.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False

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