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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 841 · 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.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  3. 3.🛠️ What helps make an AI system fairer? (a) one group only (b) fewer tests (c) ignoring mistakes (d) more varied data
  4. 4.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  5. 5.True or false: “check who is missing” adds to bias. (a) False (b) True
  6. 6.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  7. 7.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  8. 8.True or false: “skip testing” helps reduce bias. (a) False (b) True
  9. 9.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  10. 10.Which of these adds to bias? (a) add varied examples (b) ignore complaints (c) ask different people (d) test on many groups
  11. 11.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False
  12. 12.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) bias from data (c) a broken screen (d) a fair model
  13. 13.True or false: “skip testing” adds to bias. (a) True (b) False
  14. 14.Which of these adds to bias? (a) ask different people (b) copy old unfair choices (c) test on many groups (d) check who is missing
  15. 15.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  16. 16.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  17. 17.True or false: “add more of the same” adds to bias. (a) True (b) False
  18. 18.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  19. 19.🔍 Which is the odd one out? (a) check who is missing (b) add more of the same (c) skip testing (d) use one group only
  20. 20.True or false: “add varied examples” adds to bias. (a) False (b) True
  21. 21.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  22. 22.Which of these adds to bias? (a) test on one group only (b) check who is missing (c) ask different people (d) fix wrong labels
  23. 23.Which of these helps reduce bias? (a) fix wrong labels (b) copy old unfair choices (c) add more of the same (d) skip testing
  24. 24.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its colour (c) its screen size (d) the weather
  25. 25.Which of these helps reduce bias? (a) skip testing (b) check who is missing (c) copy old unfair choices (d) test on one group only
  26. 26.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  27. 27.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  28. 28.True or false: “balance the data” adds to bias. (a) True (b) False
  29. 29.Which of these helps reduce bias? (a) add more of the same (b) add varied examples (c) skip testing (d) copy old unfair choices
  30. 30.True or false: “ask different people” adds to bias. (a) True (b) False
  31. 31.🔍 Which is the odd one out? (a) use one group only (b) test on many groups (c) ignore complaints (d) test on one group only
  32. 32.Which of these helps reduce bias? (a) balance the data (b) test on one group only (c) use one group only (d) ignore complaints
  33. 33.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  34. 34.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  35. 35.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  36. 36.True or false: “ignore complaints” adds to bias. (a) True (b) False
  37. 37.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) True (b) False
  38. 38.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  39. 39.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
  40. 40.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias

Answer key

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

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