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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 302 · 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) copy old unfair choices (b) use one group only (c) balance the data (d) test on one group only
  2. 2.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) the weather (d) its colour
  3. 3.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  4. 4.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  5. 5.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  6. 6.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a fair model (c) a broken screen (d) bias from data
  7. 7.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  8. 8.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  9. 9.Which of these adds to bias? (a) ask different people (b) balance the data (c) copy old unfair choices (d) check who is missing
  10. 10.🛠️ What helps make an AI system fairer? (a) fewer tests (b) ignoring mistakes (c) one group only (d) more varied data
  11. 11.🔍 Which is the odd one out? (a) ask different people (b) test on one group only (c) fix wrong labels (d) balance the data
  12. 12.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  13. 13.True or false: “check who is missing” helps reduce bias. (a) True (b) False
  14. 14.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  15. 15.🔍 Which is the odd one out? (a) add more of the same (b) skip testing (c) add varied examples (d) use one group only
  16. 16.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  17. 17.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) few child voices in data (c) it was a rainy day (d) children speak too fast
  18. 18.🔍 Which is the odd one out? (a) ignore complaints (b) copy old unfair choices (c) fix wrong labels (d) skip testing
  19. 19.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  20. 20.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  21. 21.True or false: “ignore complaints” adds to bias. (a) True (b) False
  22. 22.🔍 Which is the odd one out? (a) test on one group only (b) ignore complaints (c) use one group only (d) balance the data
  23. 23.True or false: “skip testing” adds to bias. (a) True (b) False
  24. 24.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  25. 25.Which of these helps reduce bias? (a) use one group only (b) add varied examples (c) test on one group only (d) copy old unfair choices
  26. 26.🔍 Which is the odd one out? (a) balance the data (b) ask different people (c) use one group only (d) test on many groups
  27. 27.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  28. 28.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
  29. 29.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  30. 30.True or false: “balance the data” adds to bias. (a) True (b) False
  31. 31.Which of these helps reduce bias? (a) test on one group only (b) copy old unfair choices (c) test on many groups (d) skip testing
  32. 32.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
  33. 33.🔍 Which is the odd one out? (a) check who is missing (b) fix wrong labels (c) add varied examples (d) skip testing
  34. 34.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  35. 35.Which of these adds to bias? (a) use one group only (b) add varied examples (c) test on many groups (d) check who is missing
  36. 36.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  37. 37.True or false: “test on many groups” adds to bias. (a) True (b) False
  38. 38.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  39. 39.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
  40. 40.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias

Answer key

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

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