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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 127 · 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: “add more of the same” helps reduce bias. (a) False (b) True
  2. 2.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
  3. 3.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a fair model (c) a strong battery (d) bias from data
  4. 4.🔍 Which is the odd one out? (a) copy old unfair choices (b) check who is missing (c) ask different people (d) balance the data
  5. 5.True or false: “ask different people” helps reduce bias. (a) True (b) False
  6. 6.🔍 Which is the odd one out? (a) balance the data (b) ignore complaints (c) skip testing (d) use one group only
  7. 7.Which of these helps reduce bias? (a) use one group only (b) skip testing (c) test on one group only (d) fix wrong labels
  8. 8.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  9. 9.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  10. 10.True or false: “skip testing” adds to bias. (a) True (b) False
  11. 11.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  12. 12.Which of these adds to bias? (a) check who is missing (b) fix wrong labels (c) test on many groups (d) use one group only
  13. 13.True or false: “add more of the same” adds to bias. (a) True (b) False
  14. 14.🔍 Which is the odd one out? (a) test on many groups (b) fix wrong labels (c) ask different people (d) ignore complaints
  15. 15.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  16. 16.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) True (b) False
  17. 17.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  18. 18.True or false: “skip testing” helps reduce bias. (a) False (b) True
  19. 19.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  20. 20.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  21. 21.🔍 Which is the odd one out? (a) check who is missing (b) test on one group only (c) copy old unfair choices (d) use one group only
  22. 22.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  23. 23.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) one group only (c) more varied data (d) fewer tests
  24. 24.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  25. 25.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  26. 26.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) its colour (d) the weather
  27. 27.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) few child voices in data (c) it was a rainy day (d) children speak too fast
  28. 28.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  29. 29.Which of these helps reduce bias? (a) test on many groups (b) test on one group only (c) ignore complaints (d) copy old unfair choices
  30. 30.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
  31. 31.True or false: “balance the data” helps reduce bias. (a) False (b) True
  32. 32.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  33. 33.Which of these helps reduce bias? (a) use one group only (b) check who is missing (c) ignore complaints (d) add more of the same
  34. 34.Which of these helps reduce bias? (a) balance the data (b) test on one group only (c) copy old unfair choices (d) add more of the same
  35. 35.Which of these adds to bias? (a) copy old unfair choices (b) ask different people (c) fix wrong labels (d) balance the data
  36. 36.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  37. 37.Which of these adds to bias? (a) check who is missing (b) skip testing (c) fix wrong labels (d) add varied examples
  38. 38.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  39. 39.True or false: “add varied examples” adds to bias. (a) False (b) True
  40. 40.Which of these helps reduce bias? (a) skip testing (b) add more of the same (c) copy old unfair choices (d) add varied examples

Answer key

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

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