AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 235 · 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 of these helps reduce bias? (a) use one group only (b) ignore complaints (c) balance the data (d) skip testing
2. 2.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
3. 3.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
4. 4.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
5. 5.🔍 Which is the odd one out? (a) check who is missing (b) add more of the same (c) use one group only (d) test on one group only
6. 6.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
7. 7.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
8. 8.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
9. 9.Which of these adds to bias? (a) ask different people (b) balance the data (c) check who is missing (d) skip testing
10. 10.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
11. 11.🔍 Which is the odd one out? (a) add varied examples (b) check who is missing (c) copy old unfair choices (d) fix wrong labels
12. 12.🔍 Which is the odd one out? (a) use one group only (b) ignore complaints (c) balance the data (d) copy old unfair choices
13. 13.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
14. 14.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
15. 15.🗣️ 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
16. 16.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
17. 17.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
18. 18.True or false: “fix wrong labels” adds to bias. (a) False (b) True
19. 19.🔍 Which is the odd one out? (a) use one group only (b) test on many groups (c) add varied examples (d) ask different people
20. 20.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
21. 21.True or false: “add more of the same” helps reduce bias. (a) False (b) True
22. 22.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
23. 23.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
24. 24.🔍 Which is the odd one out? (a) skip testing (b) copy old unfair choices (c) use one group only (d) test on many groups
25. 25.Which of these helps reduce bias? (a) add more of the same (b) test on one group only (c) test on many groups (d) copy old unfair choices
26. 26.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
27. 27.🔍 Which is the odd one out? (a) ignore complaints (b) add varied examples (c) test on one group only (d) copy old unfair choices
28. 28.True or false: “ignore complaints” adds to bias. (a) True (b) False
29. 29.Which of these adds to bias? (a) add varied examples (b) ignore complaints (c) fix wrong labels (d) check who is missing
30. 30.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
31. 31.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a fair model (c) a strong battery (d) a broken screen
32. 32.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
33. 33.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
34. 34.Which of these helps reduce bias? (a) use one group only (b) check who is missing (c) skip testing (d) add more of the same
35. 35.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
36. 36.🔍 Which is the odd one out? (a) balance the data (b) ignore complaints (c) fix wrong labels (d) add varied examples
37. 37.True or false: “test on many groups” adds to bias. (a) True (b) False
38. 38.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
39. 39.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
40. 40.True or false: “use one group only” adds to bias. (a) False (b) True

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