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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 516 · 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: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
  2. 2.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  3. 3.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) few child voices in data (c) children speak too fast (d) the phone was new
  4. 4.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  5. 5.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) the weather (d) its colour
  6. 6.True or false: “ask different people” adds to bias. (a) True (b) False
  7. 7.🔍 Which is the odd one out? (a) ask different people (b) skip testing (c) test on many groups (d) balance the data
  8. 8.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  9. 9.Which of these adds to bias? (a) fix wrong labels (b) ask different people (c) copy old unfair choices (d) test on many groups
  10. 10.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
  11. 11.🔍 Which is the odd one out? (a) ask different people (b) copy old unfair choices (c) add varied examples (d) check who is missing
  12. 12.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  13. 13.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  14. 14.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  15. 15.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  16. 16.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  17. 17.🔍 Which is the odd one out? (a) skip testing (b) add more of the same (c) ignore complaints (d) test on many groups
  18. 18.Which of these helps reduce bias? (a) copy old unfair choices (b) test on many groups (c) ignore complaints (d) use one group only
  19. 19.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  20. 20.Which of these helps reduce bias? (a) use one group only (b) check who is missing (c) copy old unfair choices (d) ignore complaints
  21. 21.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  22. 22.True or false: “test on one group only” helps reduce bias. (a) False (b) True
  23. 23.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a fair model (c) bias from data (d) a broken screen
  24. 24.True or false: “copy old unfair choices” helps reduce bias. (a) False (b) True
  25. 25.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  26. 26.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  27. 27.🛠️ What helps make an AI system fairer? (a) one group only (b) fewer tests (c) more varied data (d) ignoring mistakes
  28. 28.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  29. 29.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  30. 30.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  31. 31.Which of these adds to bias? (a) test on many groups (b) check who is missing (c) add varied examples (d) use one group only
  32. 32.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  33. 33.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  34. 34.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  35. 35.Which of these adds to bias? (a) balance the data (b) add varied examples (c) fix wrong labels (d) add more of the same
  36. 36.True or false: “test on many groups” adds to bias. (a) False (b) True
  37. 37.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) False (b) True
  38. 38.🔍 Which is the odd one out? (a) copy old unfair choices (b) use one group only (c) skip testing (d) balance the data
  39. 39.True or false: “ignore complaints” adds to bias. (a) True (b) False
  40. 40.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False

Answer key

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

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