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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 572 · 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.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  2. 2.🔍 Which is the odd one out? (a) fix wrong labels (b) ignore complaints (c) add more of the same (d) use one group only
  3. 3.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  4. 4.🔍 Which is the odd one out? (a) use one group only (b) skip testing (c) add more of the same (d) test on many groups
  5. 5.🛠️ What helps make an AI system fairer? (a) more varied data (b) one group only (c) fewer tests (d) ignoring mistakes
  6. 6.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided 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.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its training data (c) its colour (d) its screen size
  9. 9.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  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.Which of these adds to bias? (a) check who is missing (b) balance the data (c) add more of the same (d) test on many groups
  12. 12.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
  13. 13.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  14. 14.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  15. 15.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  16. 16.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
  17. 17.True or false: “test on many groups” adds to bias. (a) True (b) False
  18. 18.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) the phone was new (c) children speak too fast (d) it was a rainy day
  19. 19.True or false: “test on one group only” adds to bias. (a) False (b) True
  20. 20.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a strong battery (c) bias from data (d) a broken screen
  21. 21.True or false: “use one group only” adds to bias. (a) True (b) False
  22. 22.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  23. 23.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
  24. 24.Which of these adds to bias? (a) check who is missing (b) ignore complaints (c) add varied examples (d) ask different people
  25. 25.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  26. 26.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  27. 27.🔍 Which is the odd one out? (a) use one group only (b) add more of the same (c) test on one group only (d) add varied examples
  28. 28.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  29. 29.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  30. 30.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  31. 31.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  32. 32.True or false: “add more of the same” adds to bias. (a) False (b) True
  33. 33.🔍 Which is the odd one out? (a) balance the data (b) use one group only (c) ask different people (d) add varied examples
  34. 34.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  35. 35.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
  36. 36.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  37. 37.True or false: “ignore complaints” adds to bias. (a) True (b) False
  38. 38.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  39. 39.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  40. 40.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias

Answer key

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

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