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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 354 · 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.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  2. 2.🔍 Which is the odd one out? (a) skip testing (b) balance the data (c) fix wrong labels (d) add varied examples
  3. 3.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a fair model (c) bias from data (d) a broken screen
  4. 4.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
  5. 5.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) the weather (c) its training data (d) its screen size
  6. 6.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  7. 7.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  8. 8.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  9. 9.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  10. 10.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  11. 11.True or false: “skip testing” adds to bias. (a) True (b) False
  12. 12.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
  13. 13.Which of these adds to bias? (a) balance the data (b) test on many groups (c) add varied examples (d) copy old unfair choices
  14. 14.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) more varied data (d) ignoring mistakes
  15. 15.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  16. 16.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) False (b) True
  17. 17.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  18. 18.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  19. 19.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  20. 20.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  21. 21.🔍 Which is the odd one out? (a) test on many groups (b) use one group only (c) ignore complaints (d) skip testing
  22. 22.🔍 Which is the odd one out? (a) fix wrong labels (b) ask different people (c) balance the data (d) use one group only
  23. 23.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) test on one group only (d) check who is missing
  24. 24.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  25. 25.Which of these adds to bias? (a) add varied examples (b) use one group only (c) check who is missing (d) ask different people
  26. 26.Which of these helps reduce bias? (a) use one group only (b) balance the data (c) ignore complaints (d) add more of the same
  27. 27.🔍 Which is the odd one out? (a) use one group only (b) test on one group only (c) ask different people (d) skip testing
  28. 28.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  29. 29.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  30. 30.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  31. 31.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  32. 32.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  33. 33.True or false: “test on one group only” adds to bias. (a) True (b) False
  34. 34.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  35. 35.🔍 Which is the odd one out? (a) skip testing (b) add more of the same (c) add varied examples (d) copy old unfair choices
  36. 36.True or false: “use one group only” adds to bias. (a) False (b) True
  37. 37.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  38. 38.🔍 Which is the odd one out? (a) balance the data (b) ignore complaints (c) test on many groups (d) ask different people
  39. 39.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
  40. 40.🔍 Which is the odd one out? (a) test on many groups (b) balance the data (c) add varied examples (d) copy old unfair choices

Answer key

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

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