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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 360 · 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 balanced data (fairer for everyone). (a) True (b) False
  2. 2.Which of these adds to bias? (a) check who is missing (b) test on many groups (c) ask different people (d) use one group only
  3. 3.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  4. 4.Which of these adds to bias? (a) ask different people (b) balance the data (c) fix wrong labels (d) skip testing
  5. 5.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  6. 6.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  7. 7.True or false: “test on many groups” adds to bias. (a) False (b) True
  8. 8.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  9. 9.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  10. 10.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  11. 11.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a strong battery (c) a broken screen (d) a fair model
  12. 12.True or false: “skip testing” helps reduce bias. (a) True (b) False
  13. 13.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
  14. 14.True or false: “test on one group only” adds to bias. (a) True (b) False
  15. 15.🔍 Which is the odd one out? (a) fix wrong labels (b) skip testing (c) ask different people (d) test on many groups
  16. 16.True or false: “use one group only” adds to bias. (a) False (b) True
  17. 17.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
  18. 18.🔍 Which is the odd one out? (a) check who is missing (b) ask different people (c) add varied examples (d) add more of the same
  19. 19.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  20. 20.Which of these helps reduce bias? (a) add more of the same (b) use one group only (c) fix wrong labels (d) copy old unfair choices
  21. 21.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  22. 22.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
  23. 23.🔍 Which is the odd one out? (a) check who is missing (b) copy old unfair choices (c) fix wrong labels (d) add varied examples
  24. 24.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
  25. 25.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  26. 26.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) more varied data (c) fewer tests (d) one group only
  27. 27.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  28. 28.True or false: “check who is missing” helps reduce bias. (a) True (b) False
  29. 29.True or false: “add more of the same” adds to bias. (a) True (b) False
  30. 30.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
  31. 31.True or false: “ask different people” adds to bias. (a) True (b) False
  32. 32.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  33. 33.Which of these helps reduce bias? (a) add more of the same (b) check who is missing (c) ignore complaints (d) use one group only
  34. 34.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  35. 35.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its colour (c) the weather (d) its training data
  36. 36.True or false: “ignore complaints” adds to bias. (a) False (b) True
  37. 37.🔍 Which is the odd one out? (a) copy old unfair choices (b) balance the data (c) add more of the same (d) use one group only
  38. 38.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  39. 39.Which of these adds to bias? (a) ask different people (b) balance the data (c) ignore complaints (d) check who is missing
  40. 40.True or false: “check who is missing” adds to bias. (a) True (b) False

Answer key

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

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