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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 448 · 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: “skip testing” helps reduce bias. (a) True (b) False
  2. 2.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  3. 3.True or false: “test on one group only” adds to bias. (a) False (b) True
  4. 4.True or false: “check who is missing” adds to bias. (a) False (b) True
  5. 5.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  6. 6.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a strong battery (c) a broken screen (d) bias from data
  7. 7.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  8. 8.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its colour (c) the weather (d) its training data
  9. 9.🔍 Which is the odd one out? (a) add varied examples (b) ask different people (c) test on many groups (d) test on one group only
  10. 10.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  11. 11.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  12. 12.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  13. 13.Which of these adds to bias? (a) ignore complaints (b) test on many groups (c) balance the data (d) add varied examples
  14. 14.🔍 Which is the odd one out? (a) add more of the same (b) copy old unfair choices (c) ignore complaints (d) ask different people
  15. 15.🗣️ 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) it was a rainy day (d) children speak too fast
  16. 16.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  17. 17.Which of these helps reduce bias? (a) copy old unfair choices (b) add more of the same (c) balance the data (d) test on one group only
  18. 18.🔍 Which is the odd one out? (a) balance the data (b) check who is missing (c) ask different people (d) copy old unfair choices
  19. 19.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  20. 20.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  21. 21.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  22. 22.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  23. 23.True or false: “add varied examples” adds to bias. (a) False (b) True
  24. 24.True or false: “ignore complaints” adds to bias. (a) True (b) False
  25. 25.Which of these adds to bias? (a) balance the data (b) test on many groups (c) skip testing (d) ask different people
  26. 26.Which of these adds to bias? (a) test on many groups (b) add varied examples (c) fix wrong labels (d) add more of the same
  27. 27.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  28. 28.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  29. 29.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  30. 30.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  31. 31.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  32. 32.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  33. 33.Which of these helps reduce bias? (a) ignore complaints (b) fix wrong labels (c) add more of the same (d) use one group only
  34. 34.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  35. 35.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
  36. 36.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
  37. 37.🔍 Which is the odd one out? (a) use one group only (b) balance the data (c) ask different people (d) fix wrong labels
  38. 38.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  39. 39.Which of these helps reduce bias? (a) copy old unfair choices (b) ignore complaints (c) ask different people (d) use one group only
  40. 40.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias

Answer key

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

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