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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 216 · 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.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) the phone was new (c) it was a rainy day (d) few child voices in data
  2. 2.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  3. 3.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  4. 4.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  5. 5.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a fair model (c) a strong battery (d) bias from data
  6. 6.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  7. 7.True or false: “check who is missing” adds to bias. (a) True (b) False
  8. 8.🛠️ What helps make an AI system fairer? (a) fewer tests (b) ignoring mistakes (c) one group only (d) more varied data
  9. 9.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  10. 10.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  11. 11.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  12. 12.True or false: “ignore complaints” adds to bias. (a) True (b) False
  13. 13.True or false: “ask different people” adds to bias. (a) True (b) False
  14. 14.🔍 Which is the odd one out? (a) copy old unfair choices (b) use one group only (c) add more of the same (d) check who is missing
  15. 15.Which of these adds to bias? (a) ask different people (b) add more of the same (c) balance the data (d) add varied examples
  16. 16.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  17. 17.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  18. 18.🔍 Which is the odd one out? (a) skip testing (b) fix wrong labels (c) balance the data (d) check who is missing
  19. 19.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  20. 20.True or false: “add more of the same” adds to bias. (a) True (b) False
  21. 21.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  22. 22.True or false: “use one group only” adds to bias. (a) False (b) True
  23. 23.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  24. 24.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  25. 25.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  26. 26.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  27. 27.Which of these helps reduce bias? (a) add more of the same (b) ask different people (c) ignore complaints (d) copy old unfair choices
  28. 28.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
  29. 29.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  30. 30.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  31. 31.Which of these adds to bias? (a) copy old unfair choices (b) balance the data (c) test on many groups (d) ask different people
  32. 32.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  33. 33.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its colour (c) its training data (d) the weather
  34. 34.Which of these helps reduce bias? (a) test on many groups (b) skip testing (c) ignore complaints (d) add more of the same
  35. 35.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  36. 36.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  37. 37.Which of these helps reduce bias? (a) add varied examples (b) test on one group only (c) skip testing (d) ignore complaints
  38. 38.🔍 Which is the odd one out? (a) fix wrong labels (b) balance the data (c) check who is missing (d) test on one group only
  39. 39.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  40. 40.True or false: “fix wrong labels” adds to bias. (a) False (b) True

Answer key

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

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