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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 202 · 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.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  2. 2.Which of these helps reduce bias? (a) test on one group only (b) copy old unfair choices (c) skip testing (d) test on many groups
  3. 3.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  4. 4.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a fair model (c) bias from data (d) a broken screen
  5. 5.Which of these adds to bias? (a) balance the data (b) fix wrong labels (c) check who is missing (d) ignore complaints
  6. 6.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  7. 7.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  8. 8.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  9. 9.True or false: “use one group only” helps reduce bias. (a) True (b) False
  10. 10.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  11. 11.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  12. 12.🔍 Which is the odd one out? (a) test on many groups (b) check who is missing (c) balance the data (d) copy old unfair choices
  13. 13.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  14. 14.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  15. 15.True or false: “skip testing” helps reduce bias. (a) False (b) True
  16. 16.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
  17. 17.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  18. 18.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  19. 19.Which of these helps reduce bias? (a) ignore complaints (b) ask different people (c) use one group only (d) test on one group only
  20. 20.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
  21. 21.True or false: “check who is missing” adds to bias. (a) True (b) False
  22. 22.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) the weather (c) its training data (d) its colour
  23. 23.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  24. 24.🔍 Which is the odd one out? (a) add more of the same (b) balance the data (c) use one group only (d) test on one group only
  25. 25.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  26. 26.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  27. 27.🔍 Which is the odd one out? (a) add varied examples (b) test on one group only (c) add more of the same (d) copy old unfair choices
  28. 28.Which of these helps reduce bias? (a) ignore complaints (b) use one group only (c) add varied examples (d) test on one group only
  29. 29.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  30. 30.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  31. 31.True or false: “balance the data” adds to bias. (a) True (b) False
  32. 32.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) one group only (c) more varied data (d) fewer tests
  33. 33.Which of these adds to bias? (a) add more of the same (b) balance the data (c) fix wrong labels (d) add varied examples
  34. 34.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  35. 35.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  36. 36.Which of these adds to bias? (a) check who is missing (b) fix wrong labels (c) add varied examples (d) use one group only
  37. 37.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  38. 38.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  39. 39.True or false: “test on one group only” adds to bias. (a) False (b) True
  40. 40.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias

Answer key

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

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