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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 943 · 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: skip testing? (a) adds to bias (b) reduces bias
  2. 2.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  3. 3.🔍 Which is the odd one out? (a) balance the data (b) test on many groups (c) test on one group only (d) check who is missing
  4. 4.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  5. 5.Which of these adds to bias? (a) fix wrong labels (b) skip testing (c) balance the data (d) check who is missing
  6. 6.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  7. 7.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  8. 8.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
  9. 9.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
  10. 10.🔍 Which is the odd one out? (a) test on one group only (b) copy old unfair choices (c) check who is missing (d) add more of the same
  11. 11.🔍 Which is the odd one out? (a) copy old unfair choices (b) use one group only (c) test on one group only (d) ask different people
  12. 12.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  13. 13.🔍 Which is the odd one out? (a) check who is missing (b) test on many groups (c) use one group only (d) add varied examples
  14. 14.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  15. 15.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) the weather (c) its colour (d) its screen size
  16. 16.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
  17. 17.🛠️ What helps make an AI system fairer? (a) more varied data (b) fewer tests (c) one group only (d) ignoring mistakes
  18. 18.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  19. 19.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  20. 20.True or false: “balance the data” adds to bias. (a) False (b) True
  21. 21.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  22. 22.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) children speak too fast (c) few child voices in data (d) it was a rainy day
  23. 23.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  24. 24.🔍 Which is the odd one out? (a) add varied examples (b) ask different people (c) balance the data (d) ignore complaints
  25. 25.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a broken screen (c) bias from data (d) a fair model
  26. 26.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
  27. 27.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  28. 28.True or false: “ask different people” adds to bias. (a) True (b) False
  29. 29.🔍 Which is the odd one out? (a) ask different people (b) balance the data (c) fix wrong labels (d) copy old unfair choices
  30. 30.Which of these helps reduce bias? (a) use one group only (b) test on many groups (c) ignore complaints (d) add more of the same
  31. 31.True or false: “add varied examples” adds to bias. (a) True (b) False
  32. 32.🔍 Which is the odd one out? (a) test on many groups (b) skip testing (c) add varied examples (d) fix wrong labels
  33. 33.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
  34. 34.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  35. 35.True or false: “skip testing” helps reduce bias. (a) True (b) False
  36. 36.True or false: “check who is missing” adds to bias. (a) True (b) False
  37. 37.Which of these helps reduce bias? (a) check who is missing (b) test on one group only (c) add more of the same (d) copy old unfair choices
  38. 38.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  39. 39.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) ask different people (d) use one group only
  40. 40.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data

Answer key

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

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