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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 982 · 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: add varied examples? (a) adds to bias (b) reduces bias
  2. 2.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  3. 3.🛠️ What helps make an AI system fairer? (a) one group only (b) ignoring mistakes (c) fewer tests (d) more varied data
  4. 4.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  5. 5.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  6. 6.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  7. 7.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  8. 8.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  9. 9.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a broken screen (c) a fair model (d) a strong battery
  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: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  12. 12.Which of these helps reduce bias? (a) add more of the same (b) test on one group only (c) ignore complaints (d) test on many groups
  13. 13.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  14. 14.True or false: “ignore complaints” adds to bias. (a) True (b) False
  15. 15.🔍 Which is the odd one out? (a) use one group only (b) test on many groups (c) add varied examples (d) ask different people
  16. 16.🔍 Which is the odd one out? (a) test on many groups (b) balance the data (c) check who is missing (d) add more of the same
  17. 17.🔍 Which is the odd one out? (a) use one group only (b) copy old unfair choices (c) fix wrong labels (d) ignore complaints
  18. 18.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
  19. 19.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  20. 20.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  21. 21.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  22. 22.Which of these adds to bias? (a) ask different people (b) add more of the same (c) test on many groups (d) balance the data
  23. 23.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) True (b) False
  24. 24.True or false: “add varied examples” adds to bias. (a) False (b) True
  25. 25.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  26. 26.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  27. 27.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  28. 28.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  29. 29.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  30. 30.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  31. 31.🔍 Which is the odd one out? (a) copy old unfair choices (b) use one group only (c) skip testing (d) ask different people
  32. 32.Which of these adds to bias? (a) balance the data (b) ask different people (c) copy old unfair choices (d) check who is missing
  33. 33.True or false: “skip testing” adds to bias. (a) False (b) True
  34. 34.🔍 Which is the odd one out? (a) check who is missing (b) ask different people (c) balance the data (d) ignore complaints
  35. 35.True or false: “test on many groups” adds to bias. (a) True (b) False
  36. 36.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  37. 37.Which of these helps reduce bias? (a) add more of the same (b) test on one group only (c) copy old unfair choices (d) ask different people
  38. 38.🔍 Which is the odd one out? (a) skip testing (b) check who is missing (c) add varied examples (d) ask different people
  39. 39.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
  40. 40.Which of these adds to bias? (a) ask different people (b) add varied examples (c) fix wrong labels (d) skip testing

Answer key

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

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