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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 140 · 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.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  2. 2.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  3. 3.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
  4. 4.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  5. 5.🔍 Which is the odd one out? (a) skip testing (b) balance the data (c) use one group only (d) copy old unfair choices
  6. 6.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  7. 7.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) it was a rainy day (c) few child voices in data (d) children speak too fast
  8. 8.🔍 Which is the odd one out? (a) ask different people (b) skip testing (c) test on one group only (d) add more of the same
  9. 9.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its training data (c) its colour (d) its screen size
  10. 10.True or false: “add more of the same” helps reduce bias. (a) False (b) True
  11. 11.True or false: “add more of the same” adds to bias. (a) True (b) False
  12. 12.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) check who is missing (d) use one group only
  13. 13.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  14. 14.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  15. 15.True or false: “balance the data” adds to bias. (a) False (b) True
  16. 16.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
  17. 17.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  18. 18.True or false: “add varied examples” adds to bias. (a) False (b) True
  19. 19.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  20. 20.Which of these adds to bias? (a) ask different people (b) add varied examples (c) add more of the same (d) fix wrong labels
  21. 21.Which of these helps reduce bias? (a) skip testing (b) test on one group only (c) ignore complaints (d) fix wrong labels
  22. 22.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
  23. 23.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  24. 24.True or false: “balance the data” helps reduce bias. (a) False (b) True
  25. 25.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  26. 26.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  27. 27.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  28. 28.🔍 Which is the odd one out? (a) test on one group only (b) test on many groups (c) check who is missing (d) ask different people
  29. 29.🔍 Which is the odd one out? (a) test on one group only (b) copy old unfair choices (c) skip testing (d) fix wrong labels
  30. 30.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  31. 31.🔍 Which is the odd one out? (a) add varied examples (b) ask different people (c) test on many groups (d) ignore complaints
  32. 32.🔍 Which is the odd one out? (a) add more of the same (b) add varied examples (c) copy old unfair choices (d) skip testing
  33. 33.🔍 Which is the odd one out? (a) skip testing (b) check who is missing (c) ignore complaints (d) copy old unfair choices
  34. 34.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a broken screen (c) a strong battery (d) a fair model
  35. 35.🛠️ What helps make an AI system fairer? (a) more varied data (b) ignoring mistakes (c) one group only (d) fewer tests
  36. 36.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) False (b) True
  37. 37.Which of these helps reduce bias? (a) copy old unfair choices (b) use one group only (c) skip testing (d) test on many groups
  38. 38.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  39. 39.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  40. 40.Which of these helps reduce bias? (a) ignore complaints (b) use one group only (c) copy old unfair choices (d) balance the data

Answer key

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

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