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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 660 · 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) add varied examples (b) skip testing (c) use one group only (d) ignore complaints
  2. 2.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) the weather (c) its training data (d) its colour
  3. 3.True or false: “add more of the same” adds to bias. (a) True (b) False
  4. 4.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  5. 5.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a broken screen (c) bias from data (d) a strong battery
  6. 6.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  7. 7.Which of these adds to bias? (a) fix wrong labels (b) test on many groups (c) ignore complaints (d) ask different people
  8. 8.🔍 Which is the odd one out? (a) check who is missing (b) add varied examples (c) skip testing (d) ask different people
  9. 9.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
  10. 10.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) more varied data (c) fewer tests (d) one group only
  11. 11.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  12. 12.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
  13. 13.Which of these adds to bias? (a) use one group only (b) add varied examples (c) fix wrong labels (d) balance the data
  14. 14.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  15. 15.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  16. 16.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  17. 17.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  18. 18.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  19. 19.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  20. 20.True or false: “add varied examples” helps reduce bias. (a) False (b) True
  21. 21.Which of these helps reduce bias? (a) ask different people (b) ignore complaints (c) skip testing (d) use one group only
  22. 22.🔍 Which is the odd one out? (a) use one group only (b) fix wrong labels (c) add varied examples (d) test on many groups
  23. 23.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  24. 24.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  25. 25.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) the phone was new (c) children speak too fast (d) few child voices in data
  26. 26.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
  27. 27.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  28. 28.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  29. 29.Which of these helps reduce bias? (a) use one group only (b) check who is missing (c) add more of the same (d) skip testing
  30. 30.True or false: “add varied examples” adds to bias. (a) False (b) True
  31. 31.True or false: “ask different people” adds to bias. (a) False (b) True
  32. 32.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  33. 33.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  34. 34.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  35. 35.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  36. 36.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  37. 37.Which of these helps reduce bias? (a) add more of the same (b) balance the data (c) test on one group only (d) copy old unfair choices
  38. 38.🔍 Which is the odd one out? (a) add more of the same (b) use one group only (c) test on one group only (d) ask different people
  39. 39.True or false: “skip testing” helps reduce bias. (a) False (b) True
  40. 40.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False

Answer key

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

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