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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 561 · 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) test on many groups (b) use one group only (c) ask different people (d) add varied examples
  2. 2.🔍 Which is the odd one out? (a) add varied examples (b) ignore complaints (c) copy old unfair choices (d) use one group only
  3. 3.True or false: “add more of the same” adds to bias. (a) False (b) True
  4. 4.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  5. 5.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  6. 6.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) False (b) True
  7. 7.True or false: “skip testing” helps reduce bias. (a) False (b) True
  8. 8.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
  9. 9.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  10. 10.True or false: “ask different people” adds to bias. (a) False (b) True
  11. 11.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  12. 12.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
  13. 13.🔍 Which is the odd one out? (a) copy old unfair choices (b) fix wrong labels (c) use one group only (d) add more of the same
  14. 14.🛠️ What helps make an AI system fairer? (a) one group only (b) more varied data (c) fewer tests (d) ignoring mistakes
  15. 15.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  16. 16.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  17. 17.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
  18. 18.Which of these helps reduce bias? (a) skip testing (b) test on many groups (c) test on one group only (d) ignore complaints
  19. 19.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  20. 20.True or false: “add varied examples” adds to bias. (a) False (b) True
  21. 21.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  22. 22.Which of these adds to bias? (a) add varied examples (b) test on many groups (c) add more of the same (d) ask different people
  23. 23.True or false: “check who is missing” helps reduce bias. (a) True (b) False
  24. 24.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  25. 25.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  26. 26.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its training data (c) its screen size (d) its colour
  27. 27.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a broken screen (c) a fair model (d) a strong battery
  28. 28.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  29. 29.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  30. 30.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
  31. 31.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  32. 32.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  33. 33.True or false: “use one group only” helps reduce bias. (a) False (b) True
  34. 34.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
  35. 35.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  36. 36.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  37. 37.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
  38. 38.True or false: “copy old unfair choices” helps reduce bias. (a) False (b) True
  39. 39.Which of these helps reduce bias? (a) use one group only (b) add more of the same (c) ask different people (d) copy old unfair choices
  40. 40.True or false: “skip testing” adds to bias. (a) False (b) True

Answer key

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

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