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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 712 · 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: “test on many groups” helps reduce bias. (a) True (b) False
  2. 2.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  3. 3.🛠️ What helps make an AI system fairer? (a) one group only (b) more varied data (c) fewer tests (d) ignoring mistakes
  4. 4.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
  5. 5.Which of these helps reduce bias? (a) skip testing (b) check who is missing (c) ignore complaints (d) copy old unfair choices
  6. 6.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
  7. 7.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  8. 8.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  9. 9.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) it was a rainy day (c) children speak too fast (d) the phone was new
  10. 10.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  11. 11.🔍 Which is the odd one out? (a) copy old unfair choices (b) ask different people (c) check who is missing (d) balance the data
  12. 12.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a strong battery (c) a broken screen (d) bias from data
  13. 13.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
  14. 14.Which of these adds to bias? (a) fix wrong labels (b) check who is missing (c) copy old unfair choices (d) balance the data
  15. 15.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  16. 16.Which of these helps reduce bias? (a) ignore complaints (b) skip testing (c) balance the data (d) copy old unfair choices
  17. 17.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  18. 18.True or false: “test on one group only” adds to bias. (a) True (b) False
  19. 19.True or false: “skip testing” helps reduce bias. (a) False (b) True
  20. 20.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  21. 21.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  22. 22.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its colour (c) the weather (d) its screen size
  23. 23.🔍 Which is the odd one out? (a) ignore complaints (b) check who is missing (c) balance the data (d) add varied examples
  24. 24.Which of these adds to bias? (a) add varied examples (b) ask different people (c) test on many groups (d) skip testing
  25. 25.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  26. 26.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  27. 27.Which of these adds to bias? (a) ask different people (b) test on many groups (c) use one group only (d) fix wrong labels
  28. 28.Which of these helps reduce bias? (a) test on one group only (b) add more of the same (c) fix wrong labels (d) copy old unfair choices
  29. 29.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  30. 30.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  31. 31.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  32. 32.True or false: “use one group only” helps reduce bias. (a) False (b) True
  33. 33.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  34. 34.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  35. 35.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  36. 36.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  37. 37.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  38. 38.🔍 Which is the odd one out? (a) use one group only (b) check who is missing (c) fix wrong labels (d) add varied examples
  39. 39.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
  40. 40.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True

Answer key

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

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