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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 267 · 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) fix wrong labels (b) balance the data (c) skip testing (d) ask different people
  2. 2.Which of these helps reduce bias? (a) skip testing (b) add more of the same (c) ignore complaints (d) fix wrong labels
  3. 3.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  4. 4.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  5. 5.Which of these adds to bias? (a) add varied examples (b) test on one group only (c) test on many groups (d) ask different people
  6. 6.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  7. 7.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  8. 8.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
  9. 9.Which of these helps reduce bias? (a) add more of the same (b) test on one group only (c) ignore complaints (d) balance the data
  10. 10.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  11. 11.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  12. 12.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
  13. 13.Which of these helps reduce bias? (a) use one group only (b) copy old unfair choices (c) test on one group only (d) test on many groups
  14. 14.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  15. 15.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) the weather (c) its training data (d) its colour
  16. 16.🗣️ 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
  17. 17.🔍 Which is the odd one out? (a) ask different people (b) check who is missing (c) balance the data (d) add more of the same
  18. 18.🖼️ 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
  19. 19.Which of these adds to bias? (a) test on many groups (b) add more of the same (c) ask different people (d) add varied examples
  20. 20.🔍 Which is the odd one out? (a) test on one group only (b) balance the data (c) add varied examples (d) fix wrong labels
  21. 21.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
  22. 22.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  23. 23.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
  24. 24.🔍 Which is the odd one out? (a) add more of the same (b) fix wrong labels (c) test on one group only (d) skip testing
  25. 25.Which of these adds to bias? (a) skip testing (b) add varied examples (c) ask different people (d) check who is missing
  26. 26.🛠️ What helps make an AI system fairer? (a) fewer tests (b) more varied data (c) ignoring mistakes (d) one group only
  27. 27.🔍 Which is the odd one out? (a) balance the data (b) add varied examples (c) copy old unfair choices (d) ask different people
  28. 28.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  29. 29.Which of these helps reduce bias? (a) skip testing (b) ignore complaints (c) use one group only (d) ask different people
  30. 30.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  31. 31.True or false: “use one group only” helps reduce bias. (a) True (b) False
  32. 32.🔍 Which is the odd one out? (a) copy old unfair choices (b) skip testing (c) add varied examples (d) ignore complaints
  33. 33.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  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.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  36. 36.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  37. 37.Which of these helps reduce bias? (a) copy old unfair choices (b) check who is missing (c) test on one group only (d) use one group only
  38. 38.🔍 Which is the odd one out? (a) skip testing (b) add more of the same (c) check who is missing (d) use one group only
  39. 39.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  40. 40.True or false: “balance the data” helps reduce bias. (a) False (b) True

Answer key

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

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