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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 232 · 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 is the odd one out? (a) test on many groups (b) copy old unfair choices (c) fix wrong labels (d) add varied examples
  2. 2.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  3. 3.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  4. 4.True or false: “check who is missing” adds to bias. (a) True (b) False
  5. 5.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  6. 6.🔍 Which is the odd one out? (a) copy old unfair choices (b) fix wrong labels (c) skip testing (d) ignore complaints
  7. 7.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) the phone was new (c) children speak too fast (d) it was a rainy day
  8. 8.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) False (b) True
  9. 9.🔍 Which is the odd one out? (a) copy old unfair choices (b) test on one group only (c) ignore complaints (d) test on many groups
  10. 10.Which of these helps reduce bias? (a) copy old unfair choices (b) ignore complaints (c) balance the data (d) use one group only
  11. 11.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) its training data (d) the weather
  12. 12.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) balance the data (d) copy old unfair choices
  13. 13.🔍 Which is the odd one out? (a) balance the data (b) ask different people (c) fix wrong labels (d) ignore complaints
  14. 14.Which of these helps reduce bias? (a) test on one group only (b) use one group only (c) ask different people (d) copy old unfair choices
  15. 15.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  16. 16.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  17. 17.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  18. 18.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  19. 19.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  20. 20.🛠️ What helps make an AI system fairer? (a) one group only (b) ignoring mistakes (c) fewer tests (d) more varied data
  21. 21.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  22. 22.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  23. 23.True or false: “test on many groups” adds to bias. (a) False (b) True
  24. 24.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  25. 25.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  26. 26.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  27. 27.True or false: “skip testing” adds to bias. (a) True (b) False
  28. 28.Which of these adds to bias? (a) ask different people (b) balance the data (c) add varied examples (d) add more of the same
  29. 29.🔍 Which is the odd one out? (a) ask different people (b) balance the data (c) fix wrong labels (d) add more of the same
  30. 30.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  31. 31.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  32. 32.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  33. 33.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  34. 34.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  35. 35.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  36. 36.🔍 Which is the odd one out? (a) ask different people (b) test on many groups (c) add varied examples (d) skip testing
  37. 37.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  38. 38.True or false: “copy old unfair choices” adds to bias. (a) False (b) True
  39. 39.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) bias from data (c) a strong battery (d) a fair model
  40. 40.True or false: “check who is missing” helps reduce bias. (a) False (b) True

Answer key

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

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