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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 290 · 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: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
  2. 2.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  3. 3.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  4. 4.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  5. 5.True or false: “add more of the same” adds to bias. (a) False (b) True
  6. 6.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  7. 7.🗣️ 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
  8. 8.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
  9. 9.Which of these adds to bias? (a) use one group only (b) ask different people (c) check who is missing (d) balance the data
  10. 10.Which of these helps reduce bias? (a) copy old unfair choices (b) add more of the same (c) skip testing (d) add varied examples
  11. 11.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  12. 12.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  13. 13.🔍 Which is the odd one out? (a) test on many groups (b) ignore complaints (c) ask different people (d) check who is missing
  14. 14.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) more varied data (d) ignoring mistakes
  15. 15.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  16. 16.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  17. 17.True or false: “check who is missing” helps reduce bias. (a) True (b) False
  18. 18.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
  19. 19.True or false: “add varied examples” adds to bias. (a) True (b) False
  20. 20.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) True (b) False
  21. 21.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  22. 22.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  23. 23.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  24. 24.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a broken screen (c) a fair model (d) a strong battery
  25. 25.Which of these helps reduce bias? (a) skip testing (b) ignore complaints (c) test on one group only (d) ask different people
  26. 26.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False
  27. 27.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  28. 28.True or false: “copy old unfair choices” helps reduce bias. (a) False (b) True
  29. 29.🔍 Which is the odd one out? (a) skip testing (b) test on many groups (c) test on one group only (d) ignore complaints
  30. 30.Which of these adds to bias? (a) test on many groups (b) balance the data (c) copy old unfair choices (d) check who is missing
  31. 31.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  32. 32.Which of these helps reduce bias? (a) add more of the same (b) copy old unfair choices (c) test on many groups (d) skip testing
  33. 33.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  34. 34.🔍 Which is the odd one out? (a) test on one group only (b) fix wrong labels (c) ignore complaints (d) use one group only
  35. 35.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  36. 36.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
  37. 37.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  38. 38.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its screen size (c) its colour (d) its training data
  39. 39.True or false: “skip testing” helps reduce bias. (a) True (b) False
  40. 40.Which of these helps reduce bias? (a) skip testing (b) use one group only (c) add more of the same (d) fix wrong labels

Answer key

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

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