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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 309 · 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.🔍 Which is the odd one out? (a) test on many groups (b) add more of the same (c) test on one group only (d) ignore complaints
  2. 2.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  3. 3.True or false: “balance the data” adds to bias. (a) False (b) True
  4. 4.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its training data (c) the weather (d) its screen size
  5. 5.True or false: “test on one group only” helps reduce bias. (a) False (b) True
  6. 6.True or false: “ask different people” adds to bias. (a) True (b) False
  7. 7.🔍 Which is the odd one out? (a) skip testing (b) use one group only (c) add more of the same (d) fix wrong labels
  8. 8.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  9. 9.True or false: “check who is missing” adds to bias. (a) False (b) True
  10. 10.🔍 Which is the odd one out? (a) fix wrong labels (b) test on many groups (c) skip testing (d) add varied examples
  11. 11.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  12. 12.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
  13. 13.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  14. 14.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  15. 15.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  16. 16.🔍 Which is the odd one out? (a) fix wrong labels (b) ignore complaints (c) test on many groups (d) add varied examples
  17. 17.Which of these helps reduce bias? (a) check who is missing (b) copy old unfair choices (c) skip testing (d) ignore complaints
  18. 18.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  19. 19.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  20. 20.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) bias from data (c) a fair model (d) a strong battery
  21. 21.🔍 Which is the odd one out? (a) skip testing (b) copy old unfair choices (c) add varied examples (d) ignore complaints
  22. 22.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  23. 23.🛠️ What helps make an AI system fairer? (a) fewer tests (b) more varied data (c) one group only (d) ignoring mistakes
  24. 24.True or false: “use one group only” helps reduce bias. (a) False (b) True
  25. 25.Which of these adds to bias? (a) skip testing (b) ask different people (c) add varied examples (d) check who is missing
  26. 26.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  27. 27.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  28. 28.🔍 Which is the odd one out? (a) copy old unfair choices (b) ask different people (c) fix wrong labels (d) test on many groups
  29. 29.🔍 Which is the odd one out? (a) add more of the same (b) test on one group only (c) ask different people (d) copy old unfair choices
  30. 30.Which of these adds to bias? (a) add varied examples (b) copy old unfair choices (c) fix wrong labels (d) balance the data
  31. 31.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  32. 32.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  33. 33.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
  34. 34.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  35. 35.Which of these adds to bias? (a) check who is missing (b) ask different people (c) ignore complaints (d) fix wrong labels
  36. 36.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  37. 37.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  38. 38.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  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.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data

Answer key

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

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