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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 735 · 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: “skip testing” helps reduce bias. (a) False (b) True
  2. 2.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  3. 3.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  4. 4.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  5. 5.True or false: “check who is missing” helps reduce bias. (a) True (b) False
  6. 6.Which of these helps reduce bias? (a) copy old unfair choices (b) fix wrong labels (c) add more of the same (d) skip testing
  7. 7.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  8. 8.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  9. 9.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  10. 10.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) skip testing (d) check who is missing
  11. 11.Which of these adds to bias? (a) test on many groups (b) use one group only (c) add varied examples (d) fix wrong labels
  12. 12.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  13. 13.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) bias from data (c) a broken screen (d) a strong battery
  14. 14.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its colour (c) its training data (d) the weather
  15. 15.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  16. 16.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  17. 17.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  18. 18.🛠️ What helps make an AI system fairer? (a) fewer tests (b) more varied data (c) ignoring mistakes (d) one group only
  19. 19.True or false: “copy old unfair choices” adds to bias. (a) False (b) True
  20. 20.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  21. 21.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
  22. 22.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  23. 23.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) few child voices in data (c) children speak too fast (d) the phone was new
  24. 24.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  25. 25.🔍 Which is the odd one out? (a) use one group only (b) add varied examples (c) add more of the same (d) skip testing
  26. 26.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  27. 27.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
  28. 28.Which of these adds to bias? (a) add varied examples (b) test on many groups (c) check who is missing (d) add more of the same
  29. 29.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
  30. 30.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  31. 31.True or false: “skip testing” adds to bias. (a) False (b) True
  32. 32.🔍 Which is the odd one out? (a) add varied examples (b) test on one group only (c) balance the data (d) test on many groups
  33. 33.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
  34. 34.Which of these adds to bias? (a) balance the data (b) copy old unfair choices (c) add varied examples (d) check who is missing
  35. 35.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  36. 36.Which of these adds to bias? (a) test on many groups (b) add varied examples (c) test on one group only (d) ask different people
  37. 37.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  38. 38.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
  39. 39.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  40. 40.True or false: “test on many groups” adds to bias. (a) True (b) False

Answer key

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

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