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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 717 · 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) add varied examples (b) test on many groups (c) skip testing (d) ask different people
  2. 2.🔍 Which is the odd one out? (a) copy old unfair choices (b) check who is missing (c) balance the data (d) fix wrong labels
  3. 3.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  4. 4.True or false: “balance the data” adds to bias. (a) True (b) False
  5. 5.🔍 Which is the odd one out? (a) add more of the same (b) test on many groups (c) balance the data (d) add varied examples
  6. 6.Which of these helps reduce bias? (a) ignore complaints (b) add more of the same (c) use one group only (d) balance the data
  7. 7.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
  8. 8.True or false: “test on one group only” adds to bias. (a) True (b) False
  9. 9.Which of these adds to bias? (a) ask different people (b) add varied examples (c) test on one group only (d) test on many groups
  10. 10.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a strong battery (c) a broken screen (d) bias from data
  11. 11.True or false: “add more of the same” helps reduce bias. (a) False (b) True
  12. 12.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  13. 13.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  14. 14.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False
  15. 15.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  16. 16.Which of these adds to bias? (a) check who is missing (b) fix wrong labels (c) add more of the same (d) add varied examples
  17. 17.True or false: “ask different people” helps reduce bias. (a) False (b) True
  18. 18.True or false: “test on one group only” helps reduce bias. (a) False (b) True
  19. 19.🔍 Which is the odd one out? (a) add more of the same (b) skip testing (c) check who is missing (d) test on one group only
  20. 20.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  21. 21.Which of these helps reduce bias? (a) skip testing (b) check who is missing (c) use one group only (d) add more of the same
  22. 22.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
  23. 23.🛠️ What helps make an AI system fairer? (a) one group only (b) fewer tests (c) ignoring mistakes (d) more varied data
  24. 24.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  25. 25.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  26. 26.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  27. 27.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  28. 28.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  29. 29.🔍 Which is the odd one out? (a) test on one group only (b) add varied examples (c) fix wrong labels (d) test on many groups
  30. 30.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  31. 31.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  32. 32.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
  33. 33.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  34. 34.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
  35. 35.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  36. 36.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
  37. 37.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  38. 38.Which of these helps reduce bias? (a) use one group only (b) fix wrong labels (c) copy old unfair choices (d) skip testing
  39. 39.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
  40. 40.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data

Answer key

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

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