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AI Bias Worksheet for Kids

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

Answer key

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

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