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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 877 · 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) skip testing (b) test on many groups (c) fix wrong labels (d) add varied examples
  2. 2.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  3. 3.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  4. 4.Which of these helps reduce bias? (a) use one group only (b) copy old unfair choices (c) add varied examples (d) ignore complaints
  5. 5.True or false: “ask different people” helps reduce bias. (a) False (b) True
  6. 6.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  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.Which of these adds to bias? (a) check who is missing (b) copy old unfair choices (c) balance the data (d) fix wrong labels
  9. 9.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  10. 10.True or false: “use one group only” helps reduce bias. (a) False (b) True
  11. 11.Which of these adds to bias? (a) check who is missing (b) add varied examples (c) ignore complaints (d) test on many groups
  12. 12.Which of these helps reduce bias? (a) test on many groups (b) skip testing (c) test on one group only (d) use one group only
  13. 13.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  14. 14.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  15. 15.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  16. 16.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  17. 17.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) True (b) False
  18. 18.True or false: “test on many groups” adds to bias. (a) False (b) True
  19. 19.🔍 Which is the odd one out? (a) copy old unfair choices (b) fix wrong labels (c) check who is missing (d) balance the data
  20. 20.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its training data (c) its screen size (d) the weather
  21. 21.True or false: “add varied examples” adds to bias. (a) True (b) False
  22. 22.🖼️ 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
  23. 23.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  24. 24.🔍 Which is the odd one out? (a) skip testing (b) add more of the same (c) use one group only (d) fix wrong labels
  25. 25.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
  26. 26.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  27. 27.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  28. 28.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  29. 29.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  30. 30.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  31. 31.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  32. 32.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  33. 33.Which of these helps reduce bias? (a) use one group only (b) balance the data (c) ignore complaints (d) skip testing
  34. 34.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  35. 35.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  36. 36.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) more varied data (d) ignoring mistakes
  37. 37.Which of these adds to bias? (a) check who is missing (b) fix wrong labels (c) use one group only (d) add varied examples
  38. 38.🔍 Which is the odd one out? (a) ignore complaints (b) copy old unfair choices (c) add more of the same (d) ask different people
  39. 39.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  40. 40.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True

Answer key

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

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