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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 661 · 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 with doctors of every gender? (a) one-sided data (b) balanced data
  2. 2.🔍 Which is the odd one out? (a) add varied examples (b) ignore complaints (c) copy old unfair choices (d) test on one group only
  3. 3.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  4. 4.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  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) True (b) False
  6. 6.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a fair model (c) a broken screen (d) a strong battery
  7. 7.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  8. 8.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  9. 9.True or false: “ignore complaints” adds to bias. (a) True (b) False
  10. 10.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  11. 11.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) its colour (d) the weather
  12. 12.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  13. 13.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  14. 14.🔍 Which is the odd one out? (a) test on many groups (b) ignore complaints (c) balance the data (d) fix wrong labels
  15. 15.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  16. 16.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  17. 17.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) it was a rainy day (c) the phone was new (d) children speak too fast
  18. 18.🔍 Which is the odd one out? (a) add more of the same (b) use one group only (c) test on one group only (d) ask different people
  19. 19.🔍 Which is the odd one out? (a) ignore complaints (b) copy old unfair choices (c) test on many groups (d) test on one group only
  20. 20.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  21. 21.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  22. 22.True or false: “test on many groups” adds to bias. (a) True (b) False
  23. 23.Which of these helps reduce bias? (a) add more of the same (b) balance the data (c) ignore complaints (d) test on one group only
  24. 24.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  25. 25.Which of these adds to bias? (a) add varied examples (b) balance the data (c) test on many groups (d) ignore complaints
  26. 26.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) fewer tests (c) one group only (d) more varied data
  27. 27.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  28. 28.Which of these helps reduce bias? (a) skip testing (b) test on one group only (c) check who is missing (d) use one group only
  29. 29.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  30. 30.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  31. 31.🔍 Which is the odd one out? (a) add varied examples (b) check who is missing (c) skip testing (d) test on many groups
  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.True or false: “balance the data” adds to bias. (a) False (b) True
  34. 34.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
  35. 35.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  36. 36.Which of these adds to bias? (a) test on one group only (b) ask different people (c) fix wrong labels (d) test on many groups
  37. 37.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  38. 38.True or false: “copy old unfair choices” adds to bias. (a) False (b) True
  39. 39.Which of these helps reduce bias? (a) skip testing (b) test on one group only (c) copy old unfair choices (d) add varied examples
  40. 40.True or false: “add varied examples” adds to bias. (a) False (b) True

Answer key

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

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