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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 797 · 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) fix wrong labels (b) copy old unfair choices (c) balance the data (d) check who is missing
  2. 2.🔍 Which is the odd one out? (a) balance the data (b) ignore complaints (c) test on one group only (d) skip testing
  3. 3.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  4. 4.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its screen size (c) the weather (d) its colour
  5. 5.Which of these helps reduce bias? (a) check who is missing (b) use one group only (c) copy old unfair choices (d) test on one group only
  6. 6.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  7. 7.🔍 Which is the odd one out? (a) test on one group only (b) add varied examples (c) test on many groups (d) check who is missing
  8. 8.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
  9. 9.True or false: “add varied examples” adds to bias. (a) True (b) False
  10. 10.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  11. 11.Which of these helps reduce bias? (a) copy old unfair choices (b) ignore complaints (c) ask different people (d) add more of the same
  12. 12.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  13. 13.Which of these helps reduce bias? (a) copy old unfair choices (b) ignore complaints (c) test on one group only (d) fix wrong labels
  14. 14.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  15. 15.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  16. 16.Which of these helps reduce bias? (a) add more of the same (b) skip testing (c) test on many groups (d) copy old unfair choices
  17. 17.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  18. 18.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
  19. 19.🛠️ What helps make an AI system fairer? (a) one group only (b) more varied data (c) fewer tests (d) ignoring mistakes
  20. 20.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) children speak too fast (c) the phone was new (d) few child voices in data
  21. 21.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  22. 22.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
  23. 23.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  24. 24.Which of these adds to bias? (a) test on many groups (b) check who is missing (c) ignore complaints (d) add varied examples
  25. 25.Which of these helps reduce bias? (a) skip testing (b) balance the data (c) use one group only (d) copy old unfair choices
  26. 26.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  27. 27.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a fair model (c) a broken screen (d) a strong battery
  28. 28.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  29. 29.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  30. 30.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  31. 31.🔍 Which is the odd one out? (a) ask different people (b) add more of the same (c) skip testing (d) ignore complaints
  32. 32.🔍 Which is the odd one out? (a) balance the data (b) ask different people (c) check who is missing (d) use one group only
  33. 33.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  34. 34.🔍 Which is the odd one out? (a) add varied examples (b) copy old unfair choices (c) use one group only (d) ignore complaints
  35. 35.🔍 Which is the odd one out? (a) add more of the same (b) test on many groups (c) use one group only (d) test on one group only
  36. 36.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  37. 37.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  38. 38.Which of these adds to bias? (a) test on many groups (b) test on one group only (c) balance the data (d) ask different people
  39. 39.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
  40. 40.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False

Answer key

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

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