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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 397 · 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 is the odd one out? (a) add more of the same (b) test on many groups (c) copy old unfair choices (d) skip testing
  2. 2.True or false: “test on one group only” helps reduce bias. (a) False (b) True
  3. 3.True or false: “add varied examples” adds to bias. (a) False (b) True
  4. 4.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
  5. 5.True or false: “add varied examples” helps reduce bias. (a) False (b) True
  6. 6.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  7. 7.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  8. 8.🗣️ 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) it was a rainy day (d) children speak too fast
  9. 9.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  10. 10.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  11. 11.Which of these adds to bias? (a) use one group only (b) fix wrong labels (c) ask different people (d) test on many groups
  12. 12.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  13. 13.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its screen size (c) the weather (d) its colour
  14. 14.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  15. 15.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  16. 16.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  17. 17.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  18. 18.True or false: “balance the data” adds to bias. (a) False (b) True
  19. 19.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  20. 20.🔍 Which is the odd one out? (a) skip testing (b) copy old unfair choices (c) check who is missing (d) ignore complaints
  21. 21.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  22. 22.🔍 Which is the odd one out? (a) copy old unfair choices (b) test on one group only (c) ask different people (d) ignore complaints
  23. 23.Which of these adds to bias? (a) add varied examples (b) test on many groups (c) balance the data (d) copy old unfair choices
  24. 24.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  25. 25.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  26. 26.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False
  27. 27.Which of these helps reduce bias? (a) use one group only (b) test on one group only (c) check who is missing (d) add more of the same
  28. 28.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  29. 29.🔍 Which is the odd one out? (a) balance the data (b) test on many groups (c) copy old unfair choices (d) check who is missing
  30. 30.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a fair model (c) a broken screen (d) bias from data
  31. 31.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  32. 32.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  33. 33.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  34. 34.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  35. 35.🔍 Which is the odd one out? (a) copy old unfair choices (b) balance the data (c) use one group only (d) skip testing
  36. 36.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  37. 37.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  38. 38.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  39. 39.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  40. 40.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True

Answer key

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

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