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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 574 · 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: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
  2. 2.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  3. 3.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) the phone was new (c) children speak too fast (d) few child voices in data
  4. 4.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  5. 5.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  6. 6.🖼️ 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
  7. 7.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  8. 8.True or false: “balance the data” adds to bias. (a) True (b) False
  9. 9.True or false: “skip testing” adds to bias. (a) False (b) True
  10. 10.Which of these helps reduce bias? (a) ignore complaints (b) add more of the same (c) skip testing (d) ask different people
  11. 11.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  12. 12.🔍 Which is the odd one out? (a) fix wrong labels (b) ask different people (c) copy old unfair choices (d) add varied examples
  13. 13.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  14. 14.🔍 Which is the odd one out? (a) balance the data (b) test on many groups (c) fix wrong labels (d) use one group only
  15. 15.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
  16. 16.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  17. 17.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  18. 18.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
  19. 19.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  20. 20.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  21. 21.🔍 Which is the odd one out? (a) add more of the same (b) ask different people (c) check who is missing (d) fix wrong labels
  22. 22.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  23. 23.Which of these helps reduce bias? (a) add varied examples (b) test on one group only (c) use one group only (d) skip testing
  24. 24.🛠️ What helps make an AI system fairer? (a) one group only (b) more varied data (c) ignoring mistakes (d) fewer tests
  25. 25.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  26. 26.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  27. 27.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  28. 28.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  29. 29.True or false: “ignore complaints” adds to bias. (a) True (b) False
  30. 30.Which of these helps reduce bias? (a) copy old unfair choices (b) test on one group only (c) test on many groups (d) ignore complaints
  31. 31.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  32. 32.🔍 Which is the odd one out? (a) add varied examples (b) ask different people (c) skip testing (d) test on many groups
  33. 33.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  34. 34.True or false: “check who is missing” adds to bias. (a) False (b) True
  35. 35.🔍 Which is the odd one out? (a) fix wrong labels (b) ignore complaints (c) test on many groups (d) check who is missing
  36. 36.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  37. 37.Which of these adds to bias? (a) test on many groups (b) ask different people (c) use one group only (d) add varied examples
  38. 38.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  39. 39.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  40. 40.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) False (b) True

Answer key

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

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