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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 482 · 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.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) children speak too fast (c) few child voices in data (d) the phone was new
  2. 2.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  3. 3.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  4. 4.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its screen size (c) the weather (d) its colour
  5. 5.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  6. 6.🔍 Which is the odd one out? (a) check who is missing (b) add more of the same (c) ask different people (d) balance the data
  7. 7.Which of these helps reduce bias? (a) use one group only (b) skip testing (c) add varied examples (d) add more of the same
  8. 8.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  9. 9.Which of these adds to bias? (a) balance the data (b) ask different people (c) check who is missing (d) test on one group only
  10. 10.🔍 Which is the odd one out? (a) test on many groups (b) add varied examples (c) ask different people (d) skip testing
  11. 11.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  12. 12.🔍 Which is the odd one out? (a) test on one group only (b) fix wrong labels (c) skip testing (d) ignore complaints
  13. 13.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  14. 14.Which of these adds to bias? (a) test on many groups (b) add varied examples (c) copy old unfair choices (d) ask different people
  15. 15.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  16. 16.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  17. 17.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
  18. 18.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  19. 19.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) bias from data (c) a fair model (d) a strong battery
  20. 20.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  21. 21.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  22. 22.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  23. 23.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) more varied data (c) fewer tests (d) one group only
  24. 24.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  25. 25.🔍 Which is the odd one out? (a) test on many groups (b) ignore complaints (c) add varied examples (d) check who is missing
  26. 26.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  27. 27.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  28. 28.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  29. 29.True or false: “balance the data” helps reduce bias. (a) False (b) True
  30. 30.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  31. 31.🔍 Which is the odd one out? (a) test on one group only (b) skip testing (c) add varied examples (d) copy old unfair choices
  32. 32.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  33. 33.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  34. 34.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  35. 35.Which of these adds to bias? (a) test on many groups (b) ask different people (c) use one group only (d) balance the data
  36. 36.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  37. 37.True or false: “skip testing” helps reduce bias. (a) True (b) False
  38. 38.🔍 Which is the odd one out? (a) use one group only (b) ignore complaints (c) test on one group only (d) check who is missing
  39. 39.True or false: “ignore complaints” adds to bias. (a) True (b) False
  40. 40.Which of these helps reduce bias? (a) test on many groups (b) copy old unfair choices (c) skip testing (d) use one group only

Answer key

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

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