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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 888 · 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: add varied examples? (a) reduces bias (b) adds to bias
  2. 2.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
  3. 3.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  4. 4.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  5. 5.🔍 Which is the odd one out? (a) add varied examples (b) balance the data (c) fix wrong labels (d) copy old unfair choices
  6. 6.Which of these helps reduce bias? (a) add varied examples (b) add more of the same (c) use one group only (d) test on one group only
  7. 7.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  8. 8.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  9. 9.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  10. 10.True or false: “ask different people” helps reduce bias. (a) True (b) False
  11. 11.🖼️ 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
  12. 12.True or false: “ask different people” adds to bias. (a) True (b) False
  13. 13.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) children speak too fast (c) it was a rainy day (d) the phone was new
  14. 14.Which of these helps reduce bias? (a) copy old unfair choices (b) test on one group only (c) fix wrong labels (d) skip testing
  15. 15.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  16. 16.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  17. 17.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  18. 18.🔍 Which is the odd one out? (a) ignore complaints (b) add more of the same (c) test on many groups (d) skip testing
  19. 19.Which of these helps reduce bias? (a) test on one group only (b) ask different people (c) copy old unfair choices (d) skip testing
  20. 20.True or false: “skip testing” adds to bias. (a) False (b) True
  21. 21.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  22. 22.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  23. 23.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) fewer tests (c) more varied data (d) one group only
  24. 24.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  25. 25.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  26. 26.Which of these adds to bias? (a) balance the data (b) skip testing (c) fix wrong labels (d) test on many groups
  27. 27.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  28. 28.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  29. 29.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
  30. 30.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  31. 31.True or false: “add varied examples” adds to bias. (a) True (b) False
  32. 32.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
  33. 33.True or false: “balance the data” helps reduce bias. (a) False (b) True
  34. 34.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  35. 35.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  36. 36.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  37. 37.Which of these helps reduce bias? (a) balance the data (b) use one group only (c) test on one group only (d) ignore complaints
  38. 38.🔍 Which is the odd one out? (a) skip testing (b) fix wrong labels (c) test on many groups (d) balance the data
  39. 39.🔍 Which is the odd one out? (a) balance the data (b) skip testing (c) use one group only (d) add more of the same
  40. 40.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True

Answer key

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

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