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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 274 · 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 of these adds to bias? (a) balance the data (b) fix wrong labels (c) check who is missing (d) use one group only
  2. 2.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  3. 3.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  4. 4.Which of these helps reduce bias? (a) test on one group only (b) skip testing (c) balance the data (d) use one group only
  5. 5.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
  6. 6.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  7. 7.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  8. 8.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  9. 9.🔍 Which is the odd one out? (a) check who is missing (b) skip testing (c) add varied examples (d) fix wrong labels
  10. 10.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  11. 11.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
  12. 12.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its colour (c) its screen size (d) the weather
  13. 13.Which of these adds to bias? (a) add varied examples (b) ignore complaints (c) ask different people (d) check who is missing
  14. 14.Which of these helps reduce bias? (a) ignore complaints (b) add more of the same (c) fix wrong labels (d) copy old unfair choices
  15. 15.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  16. 16.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
  17. 17.True or false: “skip testing” adds to bias. (a) False (b) True
  18. 18.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  19. 19.True or false: “test on many groups” adds to bias. (a) True (b) False
  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 strong battery (d) a fair model
  21. 21.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  22. 22.Which of these adds to bias? (a) fix wrong labels (b) balance the data (c) test on many groups (d) copy old unfair choices
  23. 23.🗣️ 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) children speak too fast (d) it was a rainy day
  24. 24.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  25. 25.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  26. 26.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  27. 27.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  28. 28.True or false: “balance the data” helps reduce bias. (a) False (b) True
  29. 29.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  30. 30.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  31. 31.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  32. 32.Which of these helps reduce bias? (a) use one group only (b) test on many groups (c) add more of the same (d) copy old unfair choices
  33. 33.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  34. 34.🔍 Which is the odd one out? (a) test on many groups (b) balance the data (c) use one group only (d) ask different people
  35. 35.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
  36. 36.True or false: “test on one group only” adds to bias. (a) False (b) True
  37. 37.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  38. 38.True or false: “ask different people” adds to bias. (a) True (b) False
  39. 39.🔍 Which is the odd one out? (a) ignore complaints (b) test on many groups (c) add varied examples (d) balance the data
  40. 40.True or false: “balance the data” adds to bias. (a) True (b) False

Answer key

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

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