AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 107 · 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) add varied examples (b) balance the data (c) skip testing (d) check who is missing
2. 2.True or false: “balance the data” adds to bias. (a) True (b) False
3. 3.🔍 Which is the odd one out? (a) balance the data (b) fix wrong labels (c) ask different people (d) skip testing
4. 4.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
5. 5.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
6. 6.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
7. 7.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
8. 8.Which of these helps reduce bias? (a) test on one group only (b) copy old unfair choices (c) balance the data (d) ignore complaints
9. 9.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
10. 10.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
11. 11.🗣️ 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
12. 12.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a strong battery (c) a fair model (d) bias from data
13. 13.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
14. 14.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
15. 15.🔍 Which is the odd one out? (a) test on many groups (b) check who is missing (c) ignore complaints (d) fix wrong labels
16. 16.🛠️ What helps make an AI system fairer? (a) one group only (b) fewer tests (c) more varied data (d) ignoring mistakes
17. 17.Which of these adds to bias? (a) add more of the same (b) ask different people (c) check who is missing (d) add varied examples
18. 18.True or false: “fix wrong labels” adds to bias. (a) False (b) True
19. 19.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
20. 20.Which of these adds to bias? (a) check who is missing (b) balance the data (c) use one group only (d) ask different people
21. 21.🔍 Which is the odd one out? (a) fix wrong labels (b) copy old unfair choices (c) add varied examples (d) test on many groups
22. 22.Which of these helps reduce bias? (a) use one group only (b) check who is missing (c) ignore complaints (d) copy old unfair choices
23. 23.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
24. 24.🔍 Which is the odd one out? (a) add more of the same (b) check who is missing (c) balance the data (d) fix wrong labels
25. 25.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) the weather (c) its colour (d) its screen size
26. 26.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
27. 27.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
28. 28.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
29. 29.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
30. 30.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
31. 31.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
32. 32.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
33. 33.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
34. 34.Which of these helps reduce bias? (a) use one group only (b) test on many groups (c) copy old unfair choices (d) test on one group only
35. 35.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
36. 36.True or false: “check who is missing” helps reduce bias. (a) False (b) True
37. 37.🔍 Which is the odd one out? (a) ignore complaints (b) skip testing (c) test on one group only (d) add varied examples
38. 38.Which of these helps reduce bias? (a) copy old unfair choices (b) ask different people (c) use one group only (d) test on one group only
39. 39.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
40. 40.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias

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