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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 931 · 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.🛠️ What helps make an AI system fairer? (a) one group only (b) ignoring mistakes (c) more varied data (d) fewer tests
  2. 2.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
  3. 3.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) balance the data (d) use one group only
  4. 4.True or false: “check who is missing” adds to bias. (a) True (b) False
  5. 5.🔍 Which is the odd one out? (a) add varied examples (b) fix wrong labels (c) test on many groups (d) test on one group only
  6. 6.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) False (b) True
  7. 7.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) children speak too fast (c) the phone was new (d) few child voices in data
  8. 8.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  9. 9.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  10. 10.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  11. 11.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  12. 12.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  13. 13.True or false: “skip testing” helps reduce bias. (a) True (b) False
  14. 14.Which of these adds to bias? (a) balance the data (b) check who is missing (c) ask different people (d) ignore complaints
  15. 15.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a strong battery (c) bias from data (d) a broken screen
  16. 16.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  17. 17.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  18. 18.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) its training data (d) the weather
  19. 19.True or false: “add varied examples” adds to bias. (a) True (b) False
  20. 20.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  21. 21.True or false: “use one group only” adds to bias. (a) True (b) False
  22. 22.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) False (b) True
  23. 23.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) False (b) True
  24. 24.🔍 Which is the odd one out? (a) balance the data (b) ignore complaints (c) test on many groups (d) ask different people
  25. 25.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  26. 26.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  27. 27.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  28. 28.Which of these adds to bias? (a) balance the data (b) test on many groups (c) ask different people (d) copy old unfair choices
  29. 29.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
  30. 30.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  31. 31.Which of these helps reduce bias? (a) use one group only (b) add more of the same (c) check who is missing (d) skip testing
  32. 32.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  33. 33.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  34. 34.Which of these helps reduce bias? (a) ignore complaints (b) use one group only (c) skip testing (d) balance the data
  35. 35.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  36. 36.Which of these adds to bias? (a) balance the data (b) test on many groups (c) skip testing (d) check who is missing
  37. 37.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  38. 38.True or false: “test on many groups” adds to bias. (a) False (b) True
  39. 39.🔍 Which is the odd one out? (a) balance the data (b) check who is missing (c) skip testing (d) test on many groups
  40. 40.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data

Answer key

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

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