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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 435 · 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) ignore complaints (b) copy old unfair choices (c) use one group only (d) ask different people
  2. 2.True or false: “use one group only” adds to bias. (a) True (b) False
  3. 3.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  4. 4.True or false: “skip testing” helps reduce bias. (a) False (b) True
  5. 5.🖼️ 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
  6. 6.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
  7. 7.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  8. 8.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) it was a rainy day (c) the phone was new (d) few child voices in data
  9. 9.🛠️ What helps make an AI system fairer? (a) more varied data (b) one group only (c) fewer tests (d) ignoring mistakes
  10. 10.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  11. 11.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  12. 12.🔍 Which is the odd one out? (a) skip testing (b) use one group only (c) balance the data (d) ignore complaints
  13. 13.🔍 Which is the odd one out? (a) ask different people (b) add varied examples (c) check who is missing (d) add more of the same
  14. 14.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) the weather (d) its training data
  15. 15.Which of these helps reduce bias? (a) check who is missing (b) test on one group only (c) skip testing (d) ignore complaints
  16. 16.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
  17. 17.🔍 Which is the odd one out? (a) fix wrong labels (b) ask different people (c) use one group only (d) test on many groups
  18. 18.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  19. 19.Which of these adds to bias? (a) check who is missing (b) test on one group only (c) fix wrong labels (d) test on many groups
  20. 20.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  21. 21.True or false: “copy old unfair choices” helps reduce bias. (a) False (b) True
  22. 22.True or false: “test on many groups” adds to bias. (a) True (b) False
  23. 23.True or false: “ignore complaints” adds to bias. (a) False (b) True
  24. 24.True or false: “add varied examples” adds to bias. (a) True (b) False
  25. 25.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  26. 26.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  27. 27.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  28. 28.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  29. 29.🔍 Which is the odd one out? (a) skip testing (b) add more of the same (c) use one group only (d) add varied examples
  30. 30.Which of these adds to bias? (a) check who is missing (b) add varied examples (c) copy old unfair choices (d) fix wrong labels
  31. 31.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
  32. 32.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
  33. 33.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  34. 34.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  35. 35.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  36. 36.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  37. 37.True or false: “add more of the same” helps reduce bias. (a) False (b) True
  38. 38.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  39. 39.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  40. 40.Which of these helps reduce bias? (a) use one group only (b) copy old unfair choices (c) ignore complaints (d) fix wrong labels

Answer key

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

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