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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 125 · 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: “test on many groups” helps reduce bias. (a) False (b) True
  2. 2.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  3. 3.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  4. 4.True or false: “test on one group only” helps reduce bias. (a) False (b) True
  5. 5.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a strong battery (c) bias from data (d) a fair model
  6. 6.True or false: “balance the data” adds to bias. (a) False (b) True
  7. 7.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  8. 8.True or false: “use one group only” helps reduce bias. (a) True (b) False
  9. 9.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  10. 10.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  11. 11.🛠️ What helps make an AI system fairer? (a) fewer tests (b) more varied data (c) one group only (d) ignoring mistakes
  12. 12.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  13. 13.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) children speak too fast (c) few child voices in data (d) it was a rainy day
  14. 14.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  15. 15.True or false: “test on many groups” adds to bias. (a) False (b) True
  16. 16.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  17. 17.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  18. 18.Which of these helps reduce bias? (a) copy old unfair choices (b) ignore complaints (c) balance the data (d) use one group only
  19. 19.🔍 Which is the odd one out? (a) ignore complaints (b) use one group only (c) copy old unfair choices (d) balance the data
  20. 20.🔍 Which is the odd one out? (a) add more of the same (b) check who is missing (c) skip testing (d) use one group only
  21. 21.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  22. 22.True or false: “skip testing” adds to bias. (a) False (b) True
  23. 23.🔍 Which is the odd one out? (a) skip testing (b) test on one group only (c) add varied examples (d) use one group only
  24. 24.Which of these helps reduce bias? (a) skip testing (b) add more of the same (c) use one group only (d) fix wrong labels
  25. 25.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) balance the data (d) test on one group only
  26. 26.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  27. 27.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  28. 28.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  29. 29.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  30. 30.🔍 Which is the odd one out? (a) use one group only (b) ignore complaints (c) fix wrong labels (d) copy old unfair choices
  31. 31.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  32. 32.Which of these adds to bias? (a) balance the data (b) fix wrong labels (c) check who is missing (d) copy old unfair choices
  33. 33.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  34. 34.Which of these helps reduce bias? (a) use one group only (b) test on one group only (c) check who is missing (d) skip testing
  35. 35.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  36. 36.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  37. 37.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  38. 38.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
  39. 39.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
  40. 40.🔍 Which is the odd one out? (a) skip testing (b) ask different people (c) copy old unfair choices (d) add more of the same

Answer key

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

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