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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 439 · 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: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  2. 2.🔍 Which is the odd one out? (a) check who is missing (b) test on many groups (c) fix wrong labels (d) ignore complaints
  3. 3.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
  4. 4.🔍 Which is the odd one out? (a) fix wrong labels (b) check who is missing (c) use one group only (d) balance the data
  5. 5.🖼️ 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
  6. 6.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
  7. 7.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  8. 8.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  9. 9.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  10. 10.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  11. 11.Which of these helps reduce bias? (a) test on one group only (b) add varied examples (c) add more of the same (d) use one group only
  12. 12.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  13. 13.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  14. 14.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  15. 15.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its training data (d) its screen size
  16. 16.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  17. 17.True or false: “test on many groups” adds to bias. (a) True (b) False
  18. 18.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  19. 19.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  20. 20.🛠️ What helps make an AI system fairer? (a) fewer tests (b) ignoring mistakes (c) more varied data (d) one group only
  21. 21.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  22. 22.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  23. 23.🔍 Which is the odd one out? (a) add varied examples (b) ignore complaints (c) add more of the same (d) test on one group only
  24. 24.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  25. 25.True or false: “add varied examples” adds to bias. (a) True (b) False
  26. 26.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  27. 27.🔍 Which is the odd one out? (a) test on one group only (b) ask different people (c) use one group only (d) ignore complaints
  28. 28.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) the phone was new (c) children speak too fast (d) it was a rainy day
  29. 29.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  30. 30.Which of these adds to bias? (a) ignore complaints (b) fix wrong labels (c) add varied examples (d) ask different people
  31. 31.Which of these adds to bias? (a) add varied examples (b) use one group only (c) ask different people (d) fix wrong labels
  32. 32.True or false: “add more of the same” adds to bias. (a) True (b) False
  33. 33.True or false: “test on one group only” helps reduce bias. (a) False (b) True
  34. 34.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  35. 35.Which of these helps reduce bias? (a) skip testing (b) test on one group only (c) use one group only (d) check who is missing
  36. 36.Which of these adds to bias? (a) check who is missing (b) skip testing (c) balance the data (d) ask different people
  37. 37.True or false: “test on one group only” adds to bias. (a) False (b) True
  38. 38.True or false: “check who is missing” adds to bias. (a) False (b) True
  39. 39.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
  40. 40.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias

Answer key

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

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