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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 131 · 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) check who is missing (b) use one group only (c) ask different people (d) balance the data
  2. 2.🔍 Which is the odd one out? (a) test on many groups (b) ask different people (c) test on one group only (d) check who is missing
  3. 3.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its training data (c) its screen size (d) its colour
  4. 4.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a strong battery (c) bias from data (d) a fair model
  5. 5.Which of these helps reduce bias? (a) test on one group only (b) test on many groups (c) skip testing (d) copy old unfair choices
  6. 6.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  7. 7.Which of these helps reduce bias? (a) use one group only (b) skip testing (c) add more of the same (d) check who is missing
  8. 8.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  9. 9.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
  10. 10.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  11. 11.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  12. 12.True or false: “ask different people” helps reduce bias. (a) False (b) True
  13. 13.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  14. 14.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  15. 15.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  16. 16.🔍 Which is the odd one out? (a) fix wrong labels (b) ignore complaints (c) balance the data (d) test on many groups
  17. 17.🛠️ What helps make an AI system fairer? (a) more varied data (b) ignoring mistakes (c) fewer tests (d) one group only
  18. 18.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  19. 19.Which of these adds to bias? (a) check who is missing (b) ask different people (c) test on many groups (d) skip testing
  20. 20.🗣️ 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
  21. 21.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  22. 22.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
  23. 23.🔍 Which is the odd one out? (a) check who is missing (b) test on many groups (c) add varied examples (d) skip testing
  24. 24.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  25. 25.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  26. 26.True or false: “use one group only” adds to bias. (a) False (b) True
  27. 27.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  28. 28.True or false: “ignore complaints” adds to bias. (a) False (b) True
  29. 29.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) True (b) False
  30. 30.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  31. 31.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  32. 32.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  33. 33.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  34. 34.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  35. 35.Which of these helps reduce bias? (a) test on one group only (b) copy old unfair choices (c) fix wrong labels (d) use one group only
  36. 36.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  37. 37.True or false: “check who is missing” adds to bias. (a) True (b) False
  38. 38.True or false: “balance the data” adds to bias. (a) True (b) False
  39. 39.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  40. 40.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias

Answer key

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

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