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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 1 · 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) fix wrong labels (b) add more of the same (c) test on one group only (d) copy old unfair choices
  2. 2.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  3. 3.🔍 Which is the odd one out? (a) add varied examples (b) ignore complaints (c) balance the data (d) check who is missing
  4. 4.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  5. 5.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) the weather (c) its screen size (d) its training data
  6. 6.True or false: “check who is missing” adds to bias. (a) True (b) False
  7. 7.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  8. 8.🔍 Which is the odd one out? (a) add varied examples (b) ask different people (c) check who is missing (d) add more of the same
  9. 9.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  10. 10.Which of these helps reduce bias? (a) copy old unfair choices (b) test on one group only (c) use one group only (d) test on many groups
  11. 11.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) bias from data (c) a broken screen (d) a strong battery
  12. 12.🔍 Which is the odd one out? (a) skip testing (b) use one group only (c) add more of the same (d) check who is missing
  13. 13.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
  14. 14.🔍 Which is the odd one out? (a) use one group only (b) test on one group only (c) ignore complaints (d) test on many groups
  15. 15.🗣️ 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
  16. 16.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
  17. 17.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  18. 18.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  19. 19.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  20. 20.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  21. 21.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  22. 22.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  23. 23.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  24. 24.🔍 Which is the odd one out? (a) test on many groups (b) skip testing (c) balance the data (d) add varied examples
  25. 25.Which of these helps reduce bias? (a) copy old unfair choices (b) use one group only (c) test on one group only (d) ask different people
  26. 26.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  27. 27.🔍 Which is the odd one out? (a) skip testing (b) ignore complaints (c) ask different people (d) test on one group only
  28. 28.True or false: “add varied examples” helps reduce bias. (a) False (b) True
  29. 29.🔍 Which is the odd one out? (a) add varied examples (b) ask different people (c) balance the data (d) test on one group only
  30. 30.Which of these helps reduce bias? (a) use one group only (b) test on one group only (c) check who is missing (d) skip testing
  31. 31.True or false: “ask different people” adds to bias. (a) True (b) False
  32. 32.Which of these adds to bias? (a) balance the data (b) ask different people (c) test on many groups (d) test on one group only
  33. 33.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  34. 34.Which of these adds to bias? (a) check who is missing (b) ignore complaints (c) add varied examples (d) fix wrong labels
  35. 35.🛠️ What helps make an AI system fairer? (a) one group only (b) more varied data (c) ignoring mistakes (d) fewer tests
  36. 36.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  37. 37.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  38. 38.🔍 Which is the odd one out? (a) add varied examples (b) add more of the same (c) use one group only (d) test on one group only
  39. 39.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  40. 40.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data

Answer key

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

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