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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 768 · 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 of these helps reduce bias? (a) test on one group only (b) ask different people (c) add more of the same (d) copy old unfair choices
  2. 2.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  3. 3.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a fair model (c) a broken screen (d) bias from data
  4. 4.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  5. 5.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its screen size (c) its colour (d) the weather
  6. 6.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) the phone was new (c) children speak too fast (d) few child voices in data
  7. 7.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  8. 8.Which of these adds to bias? (a) add varied examples (b) check who is missing (c) test on one group only (d) balance the data
  9. 9.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  10. 10.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  11. 11.True or false: “ask different people” helps reduce bias. (a) True (b) False
  12. 12.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  13. 13.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) True (b) False
  14. 14.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  15. 15.Which of these helps reduce bias? (a) ignore complaints (b) use one group only (c) skip testing (d) check who is missing
  16. 16.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
  17. 17.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  18. 18.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  19. 19.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
  20. 20.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  21. 21.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  22. 22.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  23. 23.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  24. 24.🛠️ What helps make an AI system fairer? (a) fewer tests (b) ignoring mistakes (c) more varied data (d) one group only
  25. 25.True or false: “ask different people” adds to bias. (a) True (b) False
  26. 26.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
  27. 27.🔍 Which is the odd one out? (a) balance the data (b) ignore complaints (c) test on many groups (d) ask different people
  28. 28.Which of these adds to bias? (a) add varied examples (b) test on many groups (c) ignore complaints (d) check who is missing
  29. 29.🔍 Which is the odd one out? (a) ignore complaints (b) balance the data (c) copy old unfair choices (d) test on one group only
  30. 30.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  31. 31.True or false: “use one group only” helps reduce bias. (a) False (b) True
  32. 32.🔍 Which is the odd one out? (a) skip testing (b) add varied examples (c) test on many groups (d) fix wrong labels
  33. 33.True or false: “check who is missing” adds to bias. (a) True (b) False
  34. 34.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  35. 35.🔍 Which is the odd one out? (a) use one group only (b) add more of the same (c) test on many groups (d) skip testing
  36. 36.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  37. 37.Which of these helps reduce bias? (a) ignore complaints (b) skip testing (c) balance the data (d) add more of the same
  38. 38.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
  39. 39.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  40. 40.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias

Answer key

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

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