AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 656 · 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 is the odd one out? (a) balance the data (b) ignore complaints (c) add more of the same (d) copy old unfair choices
2. 2.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
3. 3.Which of these adds to bias? (a) ask different people (b) check who is missing (c) ignore complaints (d) balance the data
4. 4.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
5. 5.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) children speak too fast (c) the phone was new (d) few child voices in data
6. 6.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) False (b) True
7. 7.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
8. 8.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
9. 9.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
10. 10.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
11. 11.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
12. 12.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) bias from data (c) a broken screen (d) a fair model
13. 13.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) ignore complaints (d) balance the data
14. 14.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
15. 15.🔍 Which is the odd one out? (a) test on many groups (b) test on one group only (c) add varied examples (d) check who is missing
16. 16.Which of these adds to bias? (a) check who is missing (b) test on many groups (c) copy old unfair choices (d) balance the data
17. 17.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
18. 18.Which of these helps reduce bias? (a) add varied examples (b) ignore complaints (c) copy old unfair choices (d) use one group only
19. 19.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
20. 20.Which of these adds to bias? (a) add varied examples (b) ask different people (c) add more of the same (d) fix wrong labels
21. 21.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
22. 22.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
23. 23.🔍 Which is the odd one out? (a) test on many groups (b) add varied examples (c) ask different people (d) copy old unfair choices
24. 24.True or false: “skip testing” helps reduce bias. (a) True (b) False
25. 25.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) ignoring mistakes (d) more varied data
26. 26.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
27. 27.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
28. 28.Which of these adds to bias? (a) test on one group only (b) fix wrong labels (c) ask different people (d) balance the data
29. 29.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
30. 30.🔍 Which is the odd one out? (a) ask different people (b) check who is missing (c) fix wrong labels (d) ignore complaints
31. 31.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
32. 32.True or false: “balance the data” helps reduce bias. (a) False (b) True
33. 33.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
34. 34.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
35. 35.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
36. 36.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
37. 37.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
38. 38.True or false: “add varied examples” adds to bias. (a) True (b) False
39. 39.Which of these helps reduce bias? (a) use one group only (b) fix wrong labels (c) test on one group only (d) copy old unfair choices
40. 40.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

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