AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 458 · 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) copy old unfair choices (b) check who is missing (c) ask different people (d) add varied examples
2. 2.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
3. 3.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
4. 4.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
5. 5.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
6. 6.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
7. 7.Which of these helps reduce bias? (a) check who is missing (b) test on one group only (c) skip testing (d) copy old unfair choices
8. 8.🔍 Which is the odd one out? (a) copy old unfair choices (b) use one group only (c) test on one group only (d) check who is missing
9. 9.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) ignoring mistakes (d) more varied data
10. 10.Which of these adds to bias? (a) add varied examples (b) ask different people (c) add more of the same (d) check who is missing
11. 11.🔍 Which is the odd one out? (a) check who is missing (b) ask different people (c) use one group only (d) test on many groups
12. 12.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) the weather (c) its training data (d) its screen size
13. 13.🔍 Which is the odd one out? (a) check who is missing (b) add varied examples (c) test on one group only (d) test on many groups
14. 14.🔍 Which is the odd one out? (a) check who is missing (b) skip testing (c) balance the data (d) add varied examples
15. 15.True or false: “use one group only” adds to bias. (a) True (b) False
16. 16.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
17. 17.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
18. 18.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
19. 19.Which of these helps reduce bias? (a) fix wrong labels (b) test on one group only (c) use one group only (d) skip testing
20. 20.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
21. 21.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
22. 22.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
23. 23.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
24. 24.True or false: “add varied examples” adds to bias. (a) True (b) False
25. 25.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
26. 26.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
27. 27.True or false: “balance the data” helps reduce bias. (a) True (b) False
28. 28.🔍 Which is the odd one out? (a) fix wrong labels (b) add more of the same (c) use one group only (d) ignore complaints
29. 29.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
30. 30.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) the phone was new (c) few child voices in data (d) it was a rainy day
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.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
33. 33.True or false: “check who is missing” helps reduce bias. (a) True (b) False
34. 34.True or false: “ask different people” adds to bias. (a) True (b) False
35. 35.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
36. 36.🔍 Which is the odd one out? (a) test on many groups (b) add varied examples (c) add more of the same (d) ask different people
37. 37.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
38. 38.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
39. 39.Which of these helps reduce bias? (a) ignore complaints (b) test on one group only (c) test on many groups (d) add more of the same
40. 40.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias

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