AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 332 · 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) fix wrong labels (b) check who is missing (c) ask different people (d) copy old unfair choices
2. 2.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its colour (c) its screen size (d) the weather
3. 3.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
4. 4.🛠️ What helps make an AI system fairer? (a) fewer tests (b) ignoring mistakes (c) more varied data (d) one group only
5. 5.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
6. 6.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
7. 7.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
8. 8.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
9. 9.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
10. 10.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) children speak too fast (c) it was a rainy day (d) few child voices in data
11. 11.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
12. 12.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
13. 13.Which of these helps reduce bias? (a) add more of the same (b) check who is missing (c) skip testing (d) test on one group only
14. 14.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
15. 15.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a broken screen (c) bias from data (d) a fair model
16. 16.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
17. 17.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
18. 18.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
19. 19.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
20. 20.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
21. 21.True or false: “test on one group only” adds to bias. (a) True (b) False
22. 22.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) False (b) True
23. 23.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
24. 24.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
25. 25.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
26. 26.🔍 Which is the odd one out? (a) test on one group only (b) add varied examples (c) ignore complaints (d) copy old unfair choices
27. 27.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
28. 28.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
29. 29.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
30. 30.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
31. 31.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
32. 32.Which of these adds to bias? (a) ask different people (b) balance the data (c) add more of the same (d) fix wrong labels
33. 33.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) ignore complaints (d) test on many groups
34. 34.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False
35. 35.🔍 Which is the odd one out? (a) skip testing (b) test on one group only (c) ask different people (d) add more of the same
36. 36.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
37. 37.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
38. 38.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
39. 39.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
40. 40.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

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