AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 26 · 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.True or false: “test on many groups” adds to bias. (a) False (b) True
2. 2.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
3. 3.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
4. 4.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
5. 5.Which of these adds to bias? (a) balance the data (b) check who is missing (c) use one group only (d) add varied examples
6. 6.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
7. 7.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
8. 8.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
9. 9.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
10. 10.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
11. 11.🔍 Which is the odd one out? (a) use one group only (b) copy old unfair choices (c) test on many groups (d) add more of the same
12. 12.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) children speak too fast (c) few child voices in data (d) it was a rainy day
13. 13.🖼️ 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
14. 14.Which of these adds to bias? (a) copy old unfair choices (b) add varied examples (c) fix wrong labels (d) test on many groups
15. 15.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
16. 16.Which of these adds to bias? (a) ask different people (b) check who is missing (c) ignore complaints (d) fix wrong labels
17. 17.Which of these helps reduce bias? (a) ask different people (b) use one group only (c) copy old unfair choices (d) skip testing
18. 18.Which of these adds to bias? (a) skip testing (b) fix wrong labels (c) add varied examples (d) balance the data
19. 19.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
20. 20.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
21. 21.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
22. 22.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
23. 23.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) True (b) False
24. 24.🔍 Which is the odd one out? (a) test on one group only (b) fix wrong labels (c) ignore complaints (d) add more of the same
25. 25.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
26. 26.True or false: “ignore complaints” adds to bias. (a) True (b) False
27. 27.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
28. 28.🔍 Which is the odd one out? (a) copy old unfair choices (b) add varied examples (c) fix wrong labels (d) check who is missing
29. 29.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
30. 30.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
31. 31.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
32. 32.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
33. 33.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
34. 34.🔍 Which is the odd one out? (a) ask different people (b) balance the data (c) check who is missing (d) skip testing
35. 35.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
36. 36.True or false: “test on one group only” helps reduce bias. (a) True (b) False
37. 37.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
38. 38.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
39. 39.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
40. 40.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) test on one group only (d) fix wrong labels

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