AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 629 · 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.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
2. 2.🔍 Which is the odd one out? (a) test on many groups (b) add varied examples (c) check who is missing (d) add more of the same
3. 3.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
4. 4.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
5. 5.Which of these adds to bias? (a) fix wrong labels (b) add varied examples (c) use one group only (d) ask different people
6. 6.Which of these adds to bias? (a) check who is missing (b) fix wrong labels (c) add more of the same (d) ask different people
7. 7.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
8. 8.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
9. 9.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
10. 10.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) one group only (c) fewer tests (d) more varied data
11. 11.🔍 Which is the odd one out? (a) test on many groups (b) use one group only (c) balance the data (d) fix wrong labels
12. 12.🗣️ 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) children speak too fast (d) the phone was new
13. 13.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
14. 14.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
15. 15.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
16. 16.Which of these helps reduce bias? (a) ignore complaints (b) skip testing (c) ask different people (d) copy old unfair choices
17. 17.Which of these helps reduce bias? (a) fix wrong labels (b) skip testing (c) use one group only (d) copy old unfair choices
18. 18.🔍 Which is the odd one out? (a) balance the data (b) ask different people (c) check who is missing (d) copy old unfair choices
19. 19.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
20. 20.Which of these helps reduce bias? (a) copy old unfair choices (b) test on many groups (c) test on one group only (d) use one group only
21. 21.🔍 Which is the odd one out? (a) test on one group only (b) skip testing (c) check who is missing (d) add more of the same
22. 22.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
23. 23.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
24. 24.Which of these helps reduce bias? (a) ignore complaints (b) skip testing (c) test on one group only (d) add varied examples
25. 25.Which of these adds to bias? (a) check who is missing (b) add varied examples (c) copy old unfair choices (d) ask different people
26. 26.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
27. 27.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
28. 28.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
29. 29.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
30. 30.True or false: “test on many groups” helps reduce bias. (a) True (b) False
31. 31.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
32. 32.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
33. 33.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
34. 34.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
35. 35.Which of these helps reduce bias? (a) use one group only (b) test on one group only (c) copy old unfair choices (d) balance the data
36. 36.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
37. 37.True or false: “balance the data” adds to bias. (a) True (b) False
38. 38.🔍 Which is the odd one out? (a) add more of the same (b) fix wrong labels (c) skip testing (d) test on one group only
39. 39.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
40. 40.True or false: “copy old unfair choices” adds to bias. (a) True (b) False

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