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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 924 · 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.Which of these helps reduce bias? (a) copy old unfair choices (b) check who is missing (c) ignore complaints (d) test on one group only
  2. 2.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  3. 3.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  4. 4.🔍 Which is the odd one out? (a) test on one group only (b) ask different people (c) ignore complaints (d) copy old unfair choices
  5. 5.True or false: “skip testing” adds to bias. (a) False (b) True
  6. 6.🛠️ What helps make an AI system fairer? (a) one group only (b) more varied data (c) ignoring mistakes (d) fewer tests
  7. 7.True or false: “add varied examples” helps reduce bias. (a) False (b) True
  8. 8.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  9. 9.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  10. 10.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) its training data (d) the weather
  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 of these adds to bias? (a) balance the data (b) ask different people (c) add varied examples (d) copy old unfair choices
  13. 13.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  14. 14.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  15. 15.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  16. 16.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  17. 17.🔍 Which is the odd one out? (a) copy old unfair choices (b) ignore complaints (c) check who is missing (d) use one group only
  18. 18.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  19. 19.🔍 Which is the odd one out? (a) ask different people (b) balance the data (c) test on one group only (d) add varied examples
  20. 20.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  21. 21.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  22. 22.🔍 Which is the odd one out? (a) copy old unfair choices (b) ignore complaints (c) add varied examples (d) use one group only
  23. 23.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  24. 24.Which of these adds to bias? (a) test on many groups (b) add varied examples (c) ignore complaints (d) fix wrong labels
  25. 25.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  26. 26.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  27. 27.🔍 Which is the odd one out? (a) fix wrong labels (b) use one group only (c) skip testing (d) copy old unfair choices
  28. 28.🔍 Which is the odd one out? (a) check who is missing (b) ignore complaints (c) balance the data (d) ask different people
  29. 29.Which of these helps reduce bias? (a) test on one group only (b) use one group only (c) skip testing (d) ask different people
  30. 30.Which of these adds to bias? (a) add varied examples (b) test on one group only (c) ask different people (d) balance the data
  31. 31.True or false: “add varied examples” adds to bias. (a) True (b) False
  32. 32.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  33. 33.True or false: “ignore complaints” adds to bias. (a) False (b) True
  34. 34.True or false: “test on many groups” adds to bias. (a) False (b) True
  35. 35.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  36. 36.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  37. 37.Which of these adds to bias? (a) test on many groups (b) balance the data (c) skip testing (d) add varied examples
  38. 38.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a strong battery (c) a fair model (d) bias from data
  39. 39.True or false: “copy old unfair choices” helps reduce bias. (a) False (b) True
  40. 40.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True

Answer key

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

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