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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 814 · 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 one group only” adds to bias. (a) False (b) True
  2. 2.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  3. 3.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  4. 4.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  5. 5.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  6. 6.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  7. 7.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  8. 8.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  9. 9.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  10. 10.Which of these adds to bias? (a) ignore complaints (b) check who is missing (c) fix wrong labels (d) add varied examples
  11. 11.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  12. 12.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  13. 13.Which of these adds to bias? (a) test on one group only (b) add varied examples (c) check who is missing (d) fix wrong labels
  14. 14.True or false: “skip testing” adds to bias. (a) True (b) False
  15. 15.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) the weather (d) its training data
  16. 16.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  17. 17.True or false: “ask different people” adds to bias. (a) True (b) False
  18. 18.🔍 Which is the odd one out? (a) skip testing (b) ignore complaints (c) test on one group only (d) add varied examples
  19. 19.True or false: “balance the data” helps reduce bias. (a) False (b) True
  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: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  22. 22.True or false: “check who is missing” helps reduce bias. (a) True (b) False
  23. 23.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  24. 24.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a broken screen (c) a fair model (d) bias from data
  25. 25.True or false: “ask different people” helps reduce bias. (a) True (b) False
  26. 26.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  27. 27.Which of these helps reduce bias? (a) test on one group only (b) ask different people (c) add more of the same (d) use one group only
  28. 28.🛠️ What helps make an AI system fairer? (a) fewer tests (b) more varied data (c) ignoring mistakes (d) one group only
  29. 29.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  30. 30.Which of these adds to bias? (a) fix wrong labels (b) ask different people (c) add varied examples (d) use one group only
  31. 31.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  32. 32.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  33. 33.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  34. 34.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  35. 35.Which of these helps reduce bias? (a) check who is missing (b) use one group only (c) copy old unfair choices (d) ignore complaints
  36. 36.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  37. 37.True or false: “check who is missing” adds to bias. (a) False (b) True
  38. 38.Which of these helps reduce bias? (a) skip testing (b) add more of the same (c) copy old unfair choices (d) balance the data
  39. 39.True or false: “add more of the same” adds to bias. (a) True (b) False
  40. 40.Which of these helps reduce bias? (a) skip testing (b) use one group only (c) copy old unfair choices (d) add varied examples

Answer key

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

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