AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 367 · 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.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
2. 2.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
3. 3.Which of these helps reduce bias? (a) copy old unfair choices (b) skip testing (c) use one group only (d) add varied examples
4. 4.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) it was a rainy day (c) the phone was new (d) few child voices in data
5. 5.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
6. 6.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
7. 7.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
8. 8.🔍 Which is the odd one out? (a) test on one group only (b) add more of the same (c) skip testing (d) balance the data
9. 9.True or false: “fix wrong labels” adds to bias. (a) False (b) True
10. 10.Which of these adds to bias? (a) ask different people (b) add varied examples (c) ignore complaints (d) test on many groups
11. 11.Which of these adds to bias? (a) fix wrong labels (b) ask different people (c) test on one group only (d) balance the data
12. 12.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
13. 13.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its training data (d) its screen size
14. 14.True or false: “add varied examples” adds to bias. (a) False (b) True
15. 15.Which of these helps reduce bias? (a) test on one group only (b) ignore complaints (c) check who is missing (d) use one group only
16. 16.True or false: “add more of the same” helps reduce bias. (a) False (b) True
17. 17.Which of these helps reduce bias? (a) test on one group only (b) skip testing (c) ignore complaints (d) fix wrong labels
18. 18.🔍 Which is the odd one out? (a) test on one group only (b) add more of the same (c) test on many groups (d) ignore complaints
19. 19.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
20. 20.🔍 Which is the odd one out? (a) test on many groups (b) test on one group only (c) ask different people (d) check who is missing
21. 21.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
22. 22.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
23. 23.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
24. 24.🔍 Which is the odd one out? (a) skip testing (b) use one group only (c) ignore complaints (d) fix wrong labels
25. 25.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
26. 26.🔍 Which is the odd one out? (a) ask different people (b) copy old unfair choices (c) add more of the same (d) use one group only
27. 27.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
28. 28.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
29. 29.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
30. 30.🔍 Which is the odd one out? (a) fix wrong labels (b) copy old unfair choices (c) add varied examples (d) check who is missing
31. 31.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
32. 32.🔍 Which is the odd one out? (a) check who is missing (b) balance the data (c) fix wrong labels (d) ignore complaints
33. 33.🔍 Which is the odd one out? (a) check who is missing (b) test on many groups (c) use one group only (d) balance the data
34. 34.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) bias from data (c) a fair model (d) a broken screen
35. 35.Which of these adds to bias? (a) check who is missing (b) add varied examples (c) balance the data (d) copy old unfair choices
36. 36.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
37. 37.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
38. 38.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
39. 39.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
40. 40.True or false: “ignore complaints” adds to bias. (a) True (b) False

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