AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 683 · 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.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
2. 2.🔍 Which is the odd one out? (a) use one group only (b) fix wrong labels (c) balance the data (d) test on many groups
3. 3.Which of these adds to bias? (a) check who is missing (b) fix wrong labels (c) add more of the same (d) add varied examples
4. 4.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
5. 5.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) children speak too fast (c) the phone was new (d) it was a rainy day
6. 6.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
7. 7.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
8. 8.True or false: “add more of the same” helps reduce bias. (a) False (b) True
9. 9.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) False (b) True
10. 10.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) balance the data (d) test on one group only
11. 11.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a fair model (c) a strong battery (d) bias from data
12. 12.🔍 Which is the odd one out? (a) test on one group only (b) copy old unfair choices (c) use one group only (d) add varied examples
13. 13.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
14. 14.🔍 Which is the odd one out? (a) balance the data (b) fix wrong labels (c) ask different people (d) skip testing
15. 15.🔍 Which is the odd one out? (a) ignore complaints (b) add more of the same (c) fix wrong labels (d) use one group only
16. 16.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
17. 17.True or false: “balance the data” helps reduce bias. (a) False (b) True
18. 18.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
19. 19.Which of these helps reduce bias? (a) test on many groups (b) copy old unfair choices (c) add more of the same (d) use one group only
20. 20.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
21. 21.True or false: “add varied examples” adds to bias. (a) False (b) True
22. 22.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
23. 23.Which of these adds to bias? (a) check who is missing (b) ask different people (c) fix wrong labels (d) ignore complaints
24. 24.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
25. 25.🔍 Which is the odd one out? (a) add more of the same (b) check who is missing (c) use one group only (d) ignore complaints
26. 26.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
27. 27.🔍 Which is the odd one out? (a) use one group only (b) add more of the same (c) ask different people (d) skip testing
28. 28.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
29. 29.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
30. 30.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
31. 31.True or false: “skip testing” adds to bias. (a) False (b) True
32. 32.Which of these helps reduce bias? (a) copy old unfair choices (b) ignore complaints (c) fix wrong labels (d) use one group only
33. 33.Which of these adds to bias? (a) ask different people (b) fix wrong labels (c) balance the data (d) use one group only
34. 34.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
35. 35.Which of these helps reduce bias? (a) check who is missing (b) test on one group only (c) copy old unfair choices (d) skip testing
36. 36.Which of these helps reduce bias? (a) test on one group only (b) balance the data (c) skip testing (d) copy old unfair choices
37. 37.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
38. 38.🔍 Which is the odd one out? (a) check who is missing (b) ask different people (c) add varied examples (d) copy old unfair choices
39. 39.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
40. 40.Which of these adds to bias? (a) test on many groups (b) fix wrong labels (c) balance the data (d) skip testing

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