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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 423 · 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 of many ages and skin tones? (a) one-sided data (b) balanced data
  2. 2.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  3. 3.True or false: “check who is missing” adds to bias. (a) True (b) False
  4. 4.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
  5. 5.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  6. 6.True or false: “add varied examples” helps reduce bias. (a) False (b) True
  7. 7.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  8. 8.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) bias from data (c) a fair model (d) a broken screen
  9. 9.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  10. 10.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its screen size (c) its colour (d) the weather
  11. 11.Which of these helps reduce bias? (a) copy old unfair choices (b) balance the data (c) skip testing (d) ignore complaints
  12. 12.Which of these helps reduce bias? (a) add more of the same (b) use one group only (c) skip testing (d) add varied examples
  13. 13.True or false: “ignore complaints” adds to bias. (a) True (b) False
  14. 14.🔍 Which is the odd one out? (a) ask different people (b) test on many groups (c) check who is missing (d) use one group only
  15. 15.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  16. 16.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  17. 17.🔍 Which is the odd one out? (a) add varied examples (b) fix wrong labels (c) test on one group only (d) ask different people
  18. 18.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  19. 19.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  20. 20.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  21. 21.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  22. 22.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  23. 23.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
  24. 24.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  25. 25.True or false: stories where every doctor is a man 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) True (b) False
  27. 27.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  28. 28.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  29. 29.Which of these helps reduce bias? (a) ignore complaints (b) ask different people (c) test on one group only (d) add more of the same
  30. 30.🔍 Which is the odd one out? (a) skip testing (b) copy old unfair choices (c) test on one group only (d) test on many groups
  31. 31.🔍 Which is the odd one out? (a) check who is missing (b) fix wrong labels (c) balance the data (d) skip testing
  32. 32.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) it was a rainy day (c) children speak too fast (d) the phone was new
  33. 33.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  34. 34.True or false: “skip testing” helps reduce bias. (a) True (b) False
  35. 35.True or false: “test on one group only” adds to bias. (a) True (b) False
  36. 36.Which of these adds to bias? (a) use one group only (b) fix wrong labels (c) check who is missing (d) add varied examples
  37. 37.🔍 Which is the odd one out? (a) balance the data (b) fix wrong labels (c) add more of the same (d) check who is missing
  38. 38.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) True (b) False
  39. 39.True or false: “add varied examples” adds to bias. (a) True (b) False
  40. 40.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data

Answer key

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

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