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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 962 · 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: “test on many groups” helps reduce bias. (a) False (b) True
  2. 2.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  3. 3.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  4. 4.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) True (b) False
  5. 5.Which of these adds to bias? (a) skip testing (b) balance the data (c) add varied examples (d) test on many groups
  6. 6.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  7. 7.True or false: “add more of the same” adds to bias. (a) True (b) False
  8. 8.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  9. 9.🔍 Which is the odd one out? (a) ask different people (b) test on many groups (c) fix wrong labels (d) use one group only
  10. 10.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  11. 11.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  12. 12.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  13. 13.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  14. 14.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
  15. 15.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its training data (c) its screen size (d) the weather
  16. 16.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
  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.True or false: “balance the data” helps reduce bias. (a) False (b) True
  19. 19.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  20. 20.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  21. 21.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  22. 22.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  23. 23.🔍 Which is the odd one out? (a) use one group only (b) copy old unfair choices (c) test on one group only (d) ask different people
  24. 24.Which of these adds to bias? (a) ask different people (b) check who is missing (c) add more of the same (d) add varied examples
  25. 25.🔍 Which is the odd one out? (a) ignore complaints (b) use one group only (c) fix wrong labels (d) add more of the same
  26. 26.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  27. 27.🔍 Which is the odd one out? (a) balance the data (b) ask different people (c) skip testing (d) check who is missing
  28. 28.🔍 Which is the odd one out? (a) balance the data (b) fix wrong labels (c) check who is missing (d) add more of the same
  29. 29.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
  30. 30.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  31. 31.True or false: “ask different people” helps reduce bias. (a) True (b) False
  32. 32.True or false: “check who is missing” adds to bias. (a) False (b) True
  33. 33.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  34. 34.True or false: “copy old unfair choices” helps reduce bias. (a) False (b) True
  35. 35.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  36. 36.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  37. 37.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  38. 38.True or false: “balance the data” adds to bias. (a) False (b) True
  39. 39.Which of these adds to bias? (a) ignore complaints (b) test on many groups (c) add varied examples (d) fix wrong labels
  40. 40.Which of these helps reduce bias? (a) ignore complaints (b) skip testing (c) check who is missing (d) copy old unfair choices

Answer key

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

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