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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 671 · 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: “ignore complaints” adds to bias. (a) False (b) True
  2. 2.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  3. 3.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) fewer tests (c) more varied data (d) one group only
  4. 4.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  5. 5.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
  6. 6.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
  7. 7.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  8. 8.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) check who is missing (d) use one group only
  9. 9.True or false: “add more of the same” adds to bias. (a) True (b) False
  10. 10.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  11. 11.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
  12. 12.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  13. 13.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
  14. 14.🔍 Which is the odd one out? (a) balance the data (b) test on many groups (c) copy old unfair choices (d) ask different people
  15. 15.True or false: “test on one group only” adds to bias. (a) False (b) True
  16. 16.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  17. 17.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) bias from data (c) a fair model (d) a broken screen
  18. 18.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  19. 19.Which of these adds to bias? (a) ask different people (b) use one group only (c) add varied examples (d) balance the data
  20. 20.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  21. 21.True or false: “add varied examples” adds to bias. (a) True (b) False
  22. 22.🔍 Which is the odd one out? (a) test on one group only (b) add more of the same (c) ask different people (d) use one group only
  23. 23.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  24. 24.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its training data (c) its screen size (d) the weather
  25. 25.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  26. 26.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
  27. 27.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) ask different people (d) skip testing
  28. 28.True or false: “ask different people” helps reduce bias. (a) False (b) True
  29. 29.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  30. 30.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  31. 31.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
  32. 32.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  33. 33.🔍 Which is the odd one out? (a) check who is missing (b) add varied examples (c) skip testing (d) balance the data
  34. 34.🔍 Which is the odd one out? (a) add varied examples (b) fix wrong labels (c) test on one group only (d) test on many groups
  35. 35.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  36. 36.True or false: “skip testing” adds to bias. (a) True (b) False
  37. 37.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) the phone was new (c) few child voices in data (d) children speak too fast
  38. 38.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  39. 39.Which of these helps reduce bias? (a) test on one group only (b) add varied examples (c) add more of the same (d) copy old unfair choices
  40. 40.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias

Answer key

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

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