AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 776 · 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 of these adds to bias? (a) test on many groups (b) ignore complaints (c) balance the data (d) add varied examples
2. 2.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) True (b) False
3. 3.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
4. 4.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
5. 5.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
6. 6.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
7. 7.🔍 Which is the odd one out? (a) copy old unfair choices (b) add varied examples (c) skip testing (d) add more of the same
8. 8.True or false: “use one group only” adds to bias. (a) True (b) False
9. 9.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
10. 10.Which of these helps reduce bias? (a) use one group only (b) add more of the same (c) copy old unfair choices (d) add varied examples
11. 11.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
12. 12.🔍 Which is the odd one out? (a) copy old unfair choices (b) fix wrong labels (c) add varied examples (d) check who is missing
13. 13.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
14. 14.Which of these adds to bias? (a) check who is missing (b) copy old unfair choices (c) add varied examples (d) fix wrong labels
15. 15.True or false: “test on one group only” helps reduce bias. (a) False (b) True
16. 16.True or false: “test on many groups” helps reduce bias. (a) False (b) True
17. 17.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) test on many groups (d) skip testing
18. 18.Which of these adds to bias? (a) check who is missing (b) balance the data (c) fix wrong labels (d) use one group only
19. 19.True or false: “skip testing” helps reduce bias. (a) False (b) True
20. 20.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
21. 21.🔍 Which is the odd one out? (a) ask different people (b) add more of the same (c) test on one group only (d) copy old unfair choices
22. 22.🔍 Which is the odd one out? (a) ask different people (b) add varied examples (c) add more of the same (d) check who is missing
23. 23.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) bias from data (c) a fair model (d) a strong battery
24. 24.🛠️ What helps make an AI system fairer? (a) more varied data (b) fewer tests (c) one group only (d) ignoring mistakes
25. 25.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) the weather (c) its training data (d) its colour
26. 26.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
27. 27.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
28. 28.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
29. 29.Which of these helps reduce bias? (a) skip testing (b) test on one group only (c) copy old unfair choices (d) ask different people
30. 30.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
31. 31.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
32. 32.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
33. 33.True or false: “check who is missing” helps reduce bias. (a) True (b) False
34. 34.Which of these helps reduce bias? (a) balance the data (b) use one group only (c) add more of the same (d) copy old unfair choices
35. 35.True or false: “add varied examples” helps reduce bias. (a) False (b) True
36. 36.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) test on one group only (d) check who is missing
37. 37.Which of these helps reduce bias? (a) add more of the same (b) test on many groups (c) copy old unfair choices (d) use one group only
38. 38.🔍 Which is the odd one out? (a) balance the data (b) copy old unfair choices (c) skip testing (d) use one group only
39. 39.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
40. 40.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias

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