AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 848 · 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: test on one group only? (a) adds to bias (b) reduces bias
2. 2.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
3. 3.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
4. 4.🔍 Which is the odd one out? (a) test on one group only (b) add more of the same (c) fix wrong labels (d) copy old unfair choices
5. 5.True or false: “test on one group only” adds to bias. (a) False (b) True
6. 6.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
7. 7.True or false: “ask different people” helps reduce bias. (a) False (b) True
8. 8.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
9. 9.🔍 Which is the odd one out? (a) check who is missing (b) add more of the same (c) ask different people (d) test on many groups
10. 10.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) more varied data (c) fewer tests (d) one group only
11. 11.Which of these adds to bias? (a) ask different people (b) fix wrong labels (c) copy old unfair choices (d) check who is missing
12. 12.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
13. 13.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
14. 14.Which of these helps reduce bias? (a) copy old unfair choices (b) skip testing (c) add more of the same (d) test on many groups
15. 15.🔍 Which is the odd one out? (a) use one group only (b) add varied examples (c) fix wrong labels (d) check who is missing
16. 16.Which of these adds to bias? (a) test on one group only (b) check who is missing (c) add varied examples (d) balance the data
17. 17.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
18. 18.Which of these adds to bias? (a) check who is missing (b) add more of the same (c) ask different people (d) add varied examples
19. 19.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
20. 20.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a strong battery (c) a fair model (d) a broken screen
21. 21.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
22. 22.True or false: “add varied examples” adds to bias. (a) True (b) False
23. 23.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
24. 24.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
25. 25.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
26. 26.Which of these helps reduce bias? (a) add more of the same (b) use one group only (c) copy old unfair choices (d) check who is missing
27. 27.True or false: “test on one group only” helps reduce bias. (a) True (b) False
28. 28.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) it was a rainy day (c) children speak too fast (d) few child voices in data
29. 29.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) the weather (d) its colour
30. 30.🔍 Which is the odd one out? (a) ignore complaints (b) add varied examples (c) balance the data (d) check who is missing
31. 31.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
32. 32.Which of these helps reduce bias? (a) skip testing (b) add varied examples (c) ignore complaints (d) copy old unfair choices
33. 33.True or false: “skip testing” helps reduce bias. (a) False (b) True
34. 34.🔍 Which is the odd one out? (a) use one group only (b) skip testing (c) test on one group only (d) balance the data
35. 35.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
36. 36.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
37. 37.True or false: “fix wrong labels” adds to bias. (a) False (b) True
38. 38.True or false: “test on many groups” helps reduce bias. (a) False (b) True
39. 39.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
40. 40.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data

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