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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 44 · 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.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  2. 2.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  3. 3.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  4. 4.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
  5. 5.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  6. 6.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  7. 7.🔍 Which is the odd one out? (a) add varied examples (b) use one group only (c) fix wrong labels (d) test on many groups
  8. 8.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
  9. 9.🔍 Which is the odd one out? (a) check who is missing (b) add more of the same (c) skip testing (d) test on one group only
  10. 10.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
  11. 11.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  12. 12.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
  13. 13.True or false: “add more of the same” adds to bias. (a) True (b) False
  14. 14.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) children speak too fast (c) it was a rainy day (d) the phone was new
  15. 15.Which of these helps reduce bias? (a) use one group only (b) test on one group only (c) skip testing (d) fix wrong labels
  16. 16.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  17. 17.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  18. 18.🛠️ What helps make an AI system fairer? (a) one group only (b) more varied data (c) fewer tests (d) ignoring mistakes
  19. 19.Which of these helps reduce bias? (a) check who is missing (b) test on one group only (c) copy old unfair choices (d) skip testing
  20. 20.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
  21. 21.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  22. 22.Which of these adds to bias? (a) copy old unfair choices (b) ask different people (c) add varied examples (d) fix wrong labels
  23. 23.🔍 Which is the odd one out? (a) add varied examples (b) fix wrong labels (c) test on one group only (d) ask different people
  24. 24.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  25. 25.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  26. 26.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  27. 27.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  28. 28.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) the weather (c) its screen size (d) its training data
  29. 29.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  30. 30.True or false: “use one group only” adds to bias. (a) False (b) True
  31. 31.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  32. 32.Which of these helps reduce bias? (a) copy old unfair choices (b) test on one group only (c) add varied examples (d) skip testing
  33. 33.🖼️ 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
  34. 34.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  35. 35.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  36. 36.True or false: “use one group only” helps reduce bias. (a) False (b) True
  37. 37.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  38. 38.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  39. 39.True or false: “add varied examples” adds to bias. (a) False (b) True
  40. 40.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False

Answer key

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

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