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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 628 · 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: test on many groups? (a) reduces bias (b) adds to bias
  2. 2.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  3. 3.Which of these helps reduce bias? (a) test on one group only (b) add varied examples (c) copy old unfair choices (d) ignore complaints
  4. 4.True or false: “ignore complaints” adds to bias. (a) False (b) True
  5. 5.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) False (b) True
  6. 6.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  7. 7.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) the weather (c) its screen size (d) its colour
  8. 8.Which of these adds to bias? (a) check who is missing (b) use one group only (c) test on many groups (d) add varied examples
  9. 9.🔍 Which is the odd one out? (a) add varied examples (b) check who is missing (c) copy old unfair choices (d) fix wrong labels
  10. 10.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
  11. 11.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  12. 12.🔍 Which is the odd one out? (a) balance the data (b) add varied examples (c) ask different people (d) test on one group only
  13. 13.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  14. 14.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  15. 15.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  16. 16.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  17. 17.🛠️ What helps make an AI system fairer? (a) one group only (b) ignoring mistakes (c) fewer tests (d) more varied data
  18. 18.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
  19. 19.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  20. 20.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  21. 21.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  22. 22.🔍 Which is the odd one out? (a) test on many groups (b) check who is missing (c) ask different people (d) use one group only
  23. 23.Which of these adds to bias? (a) ask different people (b) balance the data (c) add more of the same (d) fix wrong labels
  24. 24.Which of these helps reduce bias? (a) copy old unfair choices (b) test on many groups (c) use one group only (d) ignore complaints
  25. 25.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  26. 26.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  27. 27.Which of these helps reduce bias? (a) fix wrong labels (b) copy old unfair choices (c) skip testing (d) use one group only
  28. 28.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  29. 29.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  30. 30.True or false: “test on one group only” helps reduce bias. (a) False (b) True
  31. 31.Which of these adds to bias? (a) test on many groups (b) skip testing (c) balance the data (d) add varied examples
  32. 32.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) False (b) True
  33. 33.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a strong battery (c) a broken screen (d) a fair model
  34. 34.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  35. 35.True or false: “use one group only” adds to bias. (a) False (b) True
  36. 36.True or false: “test on many groups” adds to bias. (a) True (b) False
  37. 37.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  38. 38.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  39. 39.Which of these helps reduce bias? (a) check who is missing (b) test on one group only (c) copy old unfair choices (d) add more of the same
  40. 40.🔍 Which is the odd one out? (a) check who is missing (b) ask different people (c) ignore complaints (d) add varied examples

Answer key

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

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