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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 562 · 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) ask different people (b) add varied examples (c) use one group only (d) balance the data
  2. 2.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  3. 3.Which of these helps reduce bias? (a) add more of the same (b) add varied examples (c) use one group only (d) test on one group only
  4. 4.🔍 Which is the odd one out? (a) ask different people (b) skip testing (c) check who is missing (d) test on many groups
  5. 5.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) the weather (c) its colour (d) its screen size
  6. 6.True or false: “copy old unfair choices” adds to bias. (a) False (b) True
  7. 7.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
  8. 8.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  9. 9.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
  10. 10.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  11. 11.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) few child voices in data (c) the phone was new (d) it was a rainy day
  12. 12.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  13. 13.Which of these helps reduce bias? (a) copy old unfair choices (b) ignore complaints (c) add more of the same (d) check who is missing
  14. 14.True or false: “balance the data” adds to bias. (a) True (b) False
  15. 15.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  16. 16.🖼️ 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
  17. 17.Which of these adds to bias? (a) add varied examples (b) test on many groups (c) check who is missing (d) use one group only
  18. 18.Which of these adds to bias? (a) balance the data (b) fix wrong labels (c) test on many groups (d) skip testing
  19. 19.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  20. 20.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  21. 21.True or false: “check who is missing” adds to bias. (a) True (b) False
  22. 22.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  23. 23.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  24. 24.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  25. 25.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  26. 26.🔍 Which is the odd one out? (a) add more of the same (b) fix wrong labels (c) ask different people (d) test on many groups
  27. 27.🔍 Which is the odd one out? (a) fix wrong labels (b) skip testing (c) use one group only (d) ignore complaints
  28. 28.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  29. 29.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  30. 30.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
  31. 31.🔍 Which is the odd one out? (a) test on one group only (b) use one group only (c) skip testing (d) test on many groups
  32. 32.True or false: “ask different people” adds to bias. (a) True (b) False
  33. 33.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  34. 34.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  35. 35.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  36. 36.Which of these helps reduce bias? (a) use one group only (b) skip testing (c) add more of the same (d) balance the data
  37. 37.True or false: “use one group only” adds to bias. (a) False (b) True
  38. 38.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  39. 39.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  40. 40.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias

Answer key

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

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