AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 774 · 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.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a broken screen (c) a strong battery (d) a fair model
2. 2.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
3. 3.🛠️ What helps make an AI system fairer? (a) fewer tests (b) more varied data (c) ignoring mistakes (d) one group only
4. 4.🔍 Which is the odd one out? (a) ignore complaints (b) fix wrong labels (c) ask different people (d) check who is missing
5. 5.True or false: “ignore complaints” adds to bias. (a) True (b) False
6. 6.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
7. 7.Which of these helps reduce bias? (a) test on one group only (b) skip testing (c) test on many groups (d) add more of the same
8. 8.🔍 Which is the odd one out? (a) copy old unfair choices (b) test on one group only (c) ask different people (d) use one group only
9. 9.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
10. 10.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) its training data (d) the weather
11. 11.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
12. 12.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) the phone was new (c) children speak too fast (d) few child voices in data
13. 13.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
14. 14.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
15. 15.True or false: “check who is missing” adds to bias. (a) False (b) True
16. 16.True or false: “check who is missing” helps reduce bias. (a) False (b) True
17. 17.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
18. 18.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
19. 19.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
20. 20.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
21. 21.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
22. 22.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
23. 23.True or false: “fix wrong labels” adds to bias. (a) True (b) False
24. 24.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
25. 25.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
26. 26.True or false: “balance the data” helps reduce bias. (a) False (b) True
27. 27.🔍 Which is the odd one out? (a) ignore complaints (b) skip testing (c) add varied examples (d) test on one group only
28. 28.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
29. 29.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
30. 30.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
31. 31.Which of these adds to bias? (a) test on many groups (b) ask different people (c) ignore complaints (d) balance the data
32. 32.Which of these adds to bias? (a) add varied examples (b) balance the data (c) test on one group only (d) ask different people
33. 33.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
34. 34.🔍 Which is the odd one out? (a) skip testing (b) test on many groups (c) copy old unfair choices (d) add more of the same
35. 35.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
36. 36.🔍 Which is the odd one out? (a) add varied examples (b) check who is missing (c) test on one group only (d) balance the data
37. 37.True or false: “test on one group only” adds to bias. (a) False (b) True
38. 38.🔍 Which is the odd one out? (a) test on many groups (b) ask different people (c) copy old unfair choices (d) fix wrong labels
39. 39.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
40. 40.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True

Free ai for students lessons, practice and worksheets at talentjr.in/ai-for-students