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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 94 · 40 questions
TalentJR

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.

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

Answer key

  1. use one group only
  2. more varied data
  3. False
  4. False
  5. True
  6. one-sided data
  7. balanced data
  8. skip testing
  9. reduces bias
  10. bias from data
  11. add varied examples
  12. adds to bias
  13. one-sided data
  14. one-sided data
  15. one-sided data
  16. ignore complaints
  17. add more of the same
  18. False
  19. few child voices in data
  20. add varied examples
  21. adds to bias
  22. test on many groups
  23. balance the data
  24. True
  25. adds to bias
  26. False
  27. check who is missing
  28. False
  29. reduces bias
  30. its training data
  31. adds to bias
  32. False
  33. True
  34. False
  35. False
  36. copy old unfair choices
  37. False
  38. False
  39. balanced data
  40. test on one group only

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