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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 296 · 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.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  2. 2.True or false: “copy old unfair choices” adds to bias. (a) False (b) True
  3. 3.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  4. 4.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) the weather (d) its colour
  5. 5.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  6. 6.🛠️ What helps make an AI system fairer? (a) one group only (b) more varied data (c) ignoring mistakes (d) fewer tests
  7. 7.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  8. 8.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  9. 9.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  10. 10.🔍 Which is the odd one out? (a) use one group only (b) test on many groups (c) fix wrong labels (d) check who is missing
  11. 11.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
  12. 12.Which of these adds to bias? (a) ignore complaints (b) check who is missing (c) add varied examples (d) test on many groups
  13. 13.🔍 Which is the odd one out? (a) fix wrong labels (b) ask different people (c) add varied examples (d) skip testing
  14. 14.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
  15. 15.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  16. 16.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  17. 17.True or false: “use one group only” adds to bias. (a) False (b) True
  18. 18.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  19. 19.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  20. 20.🔍 Which is the odd one out? (a) add more of the same (b) check who is missing (c) skip testing (d) test on one group only
  21. 21.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  22. 22.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
  23. 23.Which of these helps reduce bias? (a) use one group only (b) test on one group only (c) skip testing (d) balance the data
  24. 24.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  25. 25.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
  26. 26.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  27. 27.Which of these helps reduce bias? (a) ask different people (b) use one group only (c) skip testing (d) test on one group only
  28. 28.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  29. 29.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  30. 30.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a broken screen (c) a fair model (d) a strong battery
  31. 31.🔍 Which is the odd one out? (a) add varied examples (b) copy old unfair choices (c) check who is missing (d) ask different people
  32. 32.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  33. 33.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  34. 34.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  35. 35.🔍 Which is the odd one out? (a) test on many groups (b) copy old unfair choices (c) use one group only (d) skip testing
  36. 36.Which of these adds to bias? (a) test on many groups (b) fix wrong labels (c) skip testing (d) check who is missing
  37. 37.True or false: “ignore complaints” adds to bias. (a) True (b) False
  38. 38.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
  39. 39.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) fix wrong labels (d) use one group only
  40. 40.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias

Answer key

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

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