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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 220 · 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.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a broken screen (c) bias from data (d) a strong battery
  2. 2.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  3. 3.True or false: “balance the data” adds to bias. (a) False (b) True
  4. 4.🔍 Which is the odd one out? (a) skip testing (b) test on many groups (c) use one group only (d) test on one group only
  5. 5.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  6. 6.🔍 Which is the odd one out? (a) balance the data (b) ignore complaints (c) ask different people (d) check who is missing
  7. 7.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  8. 8.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  9. 9.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its screen size (c) its colour (d) its training data
  10. 10.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) the phone was new (c) it was a rainy day (d) few child voices in data
  11. 11.🔍 Which is the odd one out? (a) copy old unfair choices (b) add varied examples (c) check who is missing (d) ask different people
  12. 12.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  13. 13.Which of these helps reduce bias? (a) add varied examples (b) test on one group only (c) add more of the same (d) copy old unfair choices
  14. 14.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  15. 15.🛠️ What helps make an AI system fairer? (a) one group only (b) fewer tests (c) ignoring mistakes (d) more varied data
  16. 16.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  17. 17.Which of these adds to bias? (a) fix wrong labels (b) ask different people (c) test on many groups (d) add more of the same
  18. 18.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  19. 19.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  20. 20.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  21. 21.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) True (b) False
  22. 22.Which of these adds to bias? (a) test on many groups (b) balance the data (c) check who is missing (d) copy old unfair choices
  23. 23.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  24. 24.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  25. 25.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  26. 26.Which of these adds to bias? (a) add varied examples (b) test on one group only (c) fix wrong labels (d) check who is missing
  27. 27.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
  28. 28.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) False (b) True
  29. 29.Which of these helps reduce bias? (a) check who is missing (b) copy old unfair choices (c) skip testing (d) add more of the same
  30. 30.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  31. 31.Which of these adds to bias? (a) balance the data (b) ignore complaints (c) ask different people (d) add varied examples
  32. 32.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (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: “skip testing” helps reduce bias. (a) False (b) True
  35. 35.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  36. 36.True or false: “add more of the same” adds to bias. (a) True (b) False
  37. 37.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  38. 38.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  39. 39.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  40. 40.True or false: “test on many groups” helps reduce bias. (a) False (b) True

Answer key

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

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