AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 507 · 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: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
2. 2.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
3. 3.🛠️ What helps make an AI system fairer? (a) one group only (b) fewer tests (c) more varied data (d) ignoring mistakes
4. 4.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) it was a rainy day (c) children speak too fast (d) the phone was new
5. 5.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
6. 6.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
7. 7.Which of these helps reduce bias? (a) use one group only (b) skip testing (c) add more of the same (d) add varied examples
8. 8.True or false: “test on many groups” helps reduce bias. (a) True (b) False
9. 9.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a fair model (c) a broken screen (d) bias from data
10. 10.🔍 Which is the odd one out? (a) add varied examples (b) balance the data (c) add more of the same (d) check who is missing
11. 11.True or false: “test on one group only” adds to bias. (a) False (b) True
12. 12.🔍 Which is the odd one out? (a) fix wrong labels (b) check who is missing (c) skip testing (d) test on many groups
13. 13.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
14. 14.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
15. 15.Which of these adds to bias? (a) ask different people (b) copy old unfair choices (c) add varied examples (d) balance the data
16. 16.🔍 Which is the odd one out? (a) test on one group only (b) skip testing (c) check who is missing (d) copy old unfair choices
17. 17.🔍 Which is the odd one out? (a) check who is missing (b) copy old unfair choices (c) test on many groups (d) fix wrong labels
18. 18.True or false: “add more of the same” helps reduce bias. (a) True (b) False
19. 19.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
20. 20.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
21. 21.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
22. 22.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
23. 23.Which of these adds to bias? (a) fix wrong labels (b) ignore complaints (c) test on many groups (d) balance the data
24. 24.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
25. 25.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
26. 26.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
27. 27.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
28. 28.Which of these helps reduce bias? (a) copy old unfair choices (b) ignore complaints (c) ask different people (d) skip testing
29. 29.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
30. 30.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
31. 31.Which of these adds to bias? (a) skip testing (b) add varied examples (c) ask different people (d) check who is missing
32. 32.🔍 Which is the odd one out? (a) balance the data (b) copy old unfair choices (c) use one group only (d) skip testing
33. 33.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
34. 34.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
35. 35.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
36. 36.Which of these helps reduce bias? (a) skip testing (b) test on many groups (c) test on one group only (d) copy old unfair choices
37. 37.🔍 Which is the odd one out? (a) copy old unfair choices (b) test on many groups (c) use one group only (d) add more of the same
38. 38.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) False (b) True
39. 39.True or false: “test on many groups” adds to bias. (a) True (b) False
40. 40.🔍 Which is the odd one out? (a) add varied examples (b) balance the data (c) fix wrong labels (d) test on one group only

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