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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 256 · 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) ignore complaints (b) test on one group only (c) add varied examples (d) add more of the same
  2. 2.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  3. 3.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  4. 4.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
  5. 5.🔍 Which is the odd one out? (a) copy old unfair choices (b) fix wrong labels (c) test on one group only (d) add more of the same
  6. 6.🔍 Which is the odd one out? (a) ignore complaints (b) use one group only (c) skip testing (d) ask different people
  7. 7.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
  8. 8.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  9. 9.Which of these helps reduce bias? (a) use one group only (b) add more of the same (c) skip testing (d) fix wrong labels
  10. 10.Which of these adds to bias? (a) add varied examples (b) add more of the same (c) fix wrong labels (d) ask different people
  11. 11.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a fair model (c) a broken screen (d) a strong battery
  12. 12.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its colour (c) the weather (d) its training data
  13. 13.True or false: “use one group only” helps reduce bias. (a) True (b) False
  14. 14.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  15. 15.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  16. 16.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  17. 17.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  18. 18.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
  19. 19.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  20. 20.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
  21. 21.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  22. 22.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  23. 23.Which of these adds to bias? (a) use one group only (b) balance the data (c) check who is missing (d) fix wrong labels
  24. 24.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
  25. 25.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  26. 26.🛠️ What helps make an AI system fairer? (a) fewer tests (b) ignoring mistakes (c) one group only (d) more varied data
  27. 27.True or false: “test on many groups” adds to bias. (a) False (b) True
  28. 28.True or false: “skip testing” adds to bias. (a) True (b) False
  29. 29.True or false: “check who is missing” adds to bias. (a) True (b) False
  30. 30.True or false: “add more of the same” helps reduce bias. (a) False (b) True
  31. 31.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  32. 32.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  33. 33.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  34. 34.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  35. 35.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  36. 36.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) True (b) False
  37. 37.True or false: “ask different people” adds to bias. (a) False (b) True
  38. 38.🔍 Which is the odd one out? (a) ask different people (b) add varied examples (c) test on many groups (d) use one group only
  39. 39.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  40. 40.Which of these adds to bias? (a) test on one group only (b) balance the data (c) check who is missing (d) test on many groups

Answer key

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

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