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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 128 · 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.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  2. 2.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  3. 3.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a fair model (c) bias from data (d) a broken screen
  4. 4.🔍 Which is the odd one out? (a) copy old unfair choices (b) ignore complaints (c) add more of the same (d) balance the data
  5. 5.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  6. 6.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
  7. 7.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  8. 8.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  9. 9.🔍 Which is the odd one out? (a) use one group only (b) ask different people (c) test on many groups (d) add varied examples
  10. 10.True or false: “balance the data” adds to bias. (a) False (b) True
  11. 11.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  12. 12.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
  13. 13.🔍 Which is the odd one out? (a) test on many groups (b) skip testing (c) balance the data (d) add varied examples
  14. 14.Which of these helps reduce bias? (a) test on one group only (b) add varied examples (c) use one group only (d) copy old unfair choices
  15. 15.🔍 Which is the odd one out? (a) add varied examples (b) fix wrong labels (c) ask different people (d) test on one group only
  16. 16.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) True (b) False
  17. 17.🛠️ What helps make an AI system fairer? (a) more varied data (b) ignoring mistakes (c) one group only (d) fewer tests
  18. 18.🔍 Which is the odd one out? (a) test on many groups (b) add varied examples (c) fix wrong labels (d) ignore complaints
  19. 19.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) children speak too fast (c) few child voices in data (d) the phone was new
  20. 20.True or false: “add varied examples” adds to bias. (a) False (b) True
  21. 21.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  22. 22.Which of these adds to bias? (a) fix wrong labels (b) ask different people (c) check who is missing (d) test on one group only
  23. 23.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  24. 24.🔍 Which is the odd one out? (a) add more of the same (b) ignore complaints (c) test on one group only (d) add varied examples
  25. 25.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  26. 26.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  27. 27.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  28. 28.Which of these helps reduce bias? (a) ask different people (b) skip testing (c) copy old unfair choices (d) use one group only
  29. 29.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  30. 30.True or false: “check who is missing” adds to bias. (a) True (b) False
  31. 31.Which of these adds to bias? (a) ask different people (b) skip testing (c) check who is missing (d) add varied examples
  32. 32.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  33. 33.Which of these adds to bias? (a) balance the data (b) test on many groups (c) add varied examples (d) ignore complaints
  34. 34.True or false: “skip testing” helps reduce bias. (a) True (b) False
  35. 35.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  36. 36.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) the weather (c) its screen size (d) its colour
  37. 37.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  38. 38.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  39. 39.🔍 Which is the odd one out? (a) fix wrong labels (b) test on many groups (c) copy old unfair choices (d) ask different people
  40. 40.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias

Answer key

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

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