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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 550 · 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: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  2. 2.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  3. 3.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) the weather (c) its training data (d) its screen size
  4. 4.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
  5. 5.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  6. 6.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a broken screen (c) bias from data (d) a strong battery
  7. 7.Which of these adds to bias? (a) ignore complaints (b) check who is missing (c) fix wrong labels (d) ask different people
  8. 8.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) False (b) True
  9. 9.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  10. 10.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  11. 11.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
  12. 12.🔍 Which is the odd one out? (a) use one group only (b) test on many groups (c) check who is missing (d) fix wrong labels
  13. 13.🔍 Which is the odd one out? (a) ignore complaints (b) add more of the same (c) copy old unfair choices (d) check who is missing
  14. 14.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  15. 15.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  16. 16.True or false: “ask different people” adds to bias. (a) False (b) True
  17. 17.True or false: “use one group only” helps reduce bias. (a) False (b) True
  18. 18.🛠️ What helps make an AI system fairer? (a) more varied data (b) ignoring mistakes (c) one group only (d) fewer tests
  19. 19.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  20. 20.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  21. 21.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  22. 22.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  23. 23.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
  24. 24.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  25. 25.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  26. 26.True or false: “test on one group only” adds to bias. (a) True (b) False
  27. 27.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  28. 28.True or false: “ignore complaints” adds to bias. (a) False (b) True
  29. 29.🔍 Which is the odd one out? (a) ask different people (b) add more of the same (c) add varied examples (d) test on many groups
  30. 30.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) False (b) True
  31. 31.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
  32. 32.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  33. 33.Which of these adds to bias? (a) fix wrong labels (b) test on many groups (c) test on one group only (d) add varied examples
  34. 34.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  35. 35.Which of these helps reduce bias? (a) ignore complaints (b) copy old unfair choices (c) add more of the same (d) add varied examples
  36. 36.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  37. 37.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) True (b) False
  38. 38.🔍 Which is the odd one out? (a) ask different people (b) test on one group only (c) balance the data (d) fix wrong labels
  39. 39.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  40. 40.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias

Answer key

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

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