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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 126 · 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.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  2. 2.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  3. 3.🔍 Which is the odd one out? (a) test on many groups (b) ask different people (c) ignore complaints (d) check who is missing
  4. 4.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) the phone was new (c) it was a rainy day (d) children speak too fast
  5. 5.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  6. 6.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  7. 7.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its training data (c) the weather (d) its screen size
  8. 8.🔍 Which is the odd one out? (a) test on many groups (b) add varied examples (c) ask different people (d) copy old unfair choices
  9. 9.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  10. 10.Which of these helps reduce bias? (a) add more of the same (b) copy old unfair choices (c) test on one group only (d) test on many groups
  11. 11.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  12. 12.🔍 Which is the odd one out? (a) ask different people (b) test on many groups (c) add varied examples (d) test on one group only
  13. 13.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  14. 14.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  15. 15.True or false: “use one group only” adds to bias. (a) True (b) False
  16. 16.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  17. 17.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  18. 18.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) ignoring mistakes (d) more varied data
  19. 19.🔍 Which is the odd one out? (a) test on many groups (b) fix wrong labels (c) use one group only (d) check who is missing
  20. 20.🔍 Which is the odd one out? (a) fix wrong labels (b) skip testing (c) check who is missing (d) balance the data
  21. 21.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) False (b) True
  22. 22.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  23. 23.Which of these helps reduce bias? (a) copy old unfair choices (b) add more of the same (c) fix wrong labels (d) use one group only
  24. 24.Which of these adds to bias? (a) check who is missing (b) balance the data (c) add varied examples (d) add more of the same
  25. 25.🔍 Which is the odd one out? (a) test on one group only (b) test on many groups (c) ignore complaints (d) copy old unfair choices
  26. 26.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
  27. 27.🔍 Which is the odd one out? (a) add more of the same (b) ignore complaints (c) skip testing (d) balance the data
  28. 28.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) check who is missing (d) ignore complaints
  29. 29.Which of these adds to bias? (a) add varied examples (b) test on many groups (c) check who is missing (d) copy old unfair choices
  30. 30.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  31. 31.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  32. 32.True or false: “add more of the same” adds to bias. (a) False (b) True
  33. 33.🔍 Which is the odd one out? (a) copy old unfair choices (b) add more of the same (c) ask different people (d) use one group only
  34. 34.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  35. 35.🖼️ 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
  36. 36.🔍 Which is the odd one out? (a) ignore complaints (b) add varied examples (c) add more of the same (d) skip testing
  37. 37.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  38. 38.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  39. 39.Which of these helps reduce bias? (a) add varied examples (b) add more of the same (c) copy old unfair choices (d) skip testing
  40. 40.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) check who is missing (d) add more of the same

Answer key

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

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