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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 71 · 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 is the odd one out? (a) test on many groups (b) balance the data (c) fix wrong labels (d) test on one group only
  2. 2.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  3. 3.Which of these helps reduce bias? (a) ignore complaints (b) use one group only (c) balance the data (d) add more of the same
  4. 4.True or false: “skip testing” adds to bias. (a) True (b) False
  5. 5.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  6. 6.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  7. 7.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  8. 8.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  9. 9.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  10. 10.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
  11. 11.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  12. 12.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  13. 13.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  14. 14.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  15. 15.True or false: “use one group only” helps reduce bias. (a) True (b) False
  16. 16.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  17. 17.🔍 Which is the odd one out? (a) test on many groups (b) balance the data (c) fix wrong labels (d) use one group only
  18. 18.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a fair model (c) a strong battery (d) a broken screen
  19. 19.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
  20. 20.True or false: “check who is missing” adds to bias. (a) True (b) False
  21. 21.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) the weather (d) its training data
  22. 22.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  23. 23.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  24. 24.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  25. 25.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  26. 26.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) it was a rainy day (c) few child voices in data (d) children speak too fast
  27. 27.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  28. 28.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
  29. 29.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  30. 30.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  31. 31.True or false: “use one group only” adds to bias. (a) True (b) False
  32. 32.🛠️ What helps make an AI system fairer? (a) more varied data (b) fewer tests (c) ignoring mistakes (d) one group only
  33. 33.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  34. 34.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  35. 35.🔍 Which is the odd one out? (a) skip testing (b) add more of the same (c) use one group only (d) add varied examples
  36. 36.Which of these adds to bias? (a) use one group only (b) balance the data (c) fix wrong labels (d) ask different people
  37. 37.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  38. 38.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  39. 39.🔍 Which is the odd one out? (a) test on many groups (b) skip testing (c) fix wrong labels (d) add varied examples
  40. 40.Which of these helps reduce bias? (a) fix wrong labels (b) test on one group only (c) use one group only (d) add more of the same

Answer key

  1. test on one group only
  2. balanced data
  3. balance the data
  4. True
  5. True
  6. one-sided data
  7. reduces bias
  8. adds to bias
  9. one-sided data
  10. False
  11. False
  12. adds to bias
  13. one-sided data
  14. adds to bias
  15. False
  16. False
  17. use one group only
  18. bias from data
  19. True
  20. False
  21. its training data
  22. balanced data
  23. False
  24. one-sided data
  25. balanced data
  26. few child voices in data
  27. False
  28. True
  29. balanced data
  30. adds to bias
  31. True
  32. more varied data
  33. True
  34. balanced data
  35. add varied examples
  36. use one group only
  37. reduces bias
  38. reduces bias
  39. skip testing
  40. fix wrong labels

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