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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 205 · 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: neat and messy writing from many people? (a) one-sided data (b) balanced data
  2. 2.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  3. 3.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
  4. 4.Which of these adds to bias? (a) test on many groups (b) use one group only (c) check who is missing (d) ask different people
  5. 5.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  6. 6.True or false: “ask different people” adds to bias. (a) False (b) True
  7. 7.🛠️ What helps make an AI system fairer? (a) one group only (b) more varied data (c) ignoring mistakes (d) fewer tests
  8. 8.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  9. 9.True or false: “add varied examples” adds to bias. (a) True (b) False
  10. 10.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  11. 11.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  12. 12.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) children speak too fast (c) it was a rainy day (d) few child voices in data
  13. 13.True or false: “test on one group only” adds to bias. (a) False (b) True
  14. 14.Which of these helps reduce bias? (a) test on one group only (b) skip testing (c) ask different people (d) ignore complaints
  15. 15.🔍 Which is the odd one out? (a) check who is missing (b) fix wrong labels (c) skip testing (d) add varied examples
  16. 16.True or false: “copy old unfair choices” adds to bias. (a) False (b) True
  17. 17.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) bias from data (c) a broken screen (d) a fair model
  18. 18.Which of these adds to bias? (a) check who is missing (b) fix wrong labels (c) balance the data (d) copy old unfair choices
  19. 19.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  20. 20.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its screen size (c) the weather (d) its colour
  21. 21.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  22. 22.Which of these adds to bias? (a) ask different people (b) test on many groups (c) check who is missing (d) add more of the same
  23. 23.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  24. 24.Which of these helps reduce bias? (a) add more of the same (b) add varied examples (c) test on one group only (d) copy old unfair choices
  25. 25.🔍 Which is the odd one out? (a) skip testing (b) add more of the same (c) ignore complaints (d) balance the data
  26. 26.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  27. 27.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  28. 28.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  29. 29.🔍 Which is the odd one out? (a) fix wrong labels (b) ignore complaints (c) test on many groups (d) balance the data
  30. 30.Which of these adds to bias? (a) skip testing (b) ask different people (c) fix wrong labels (d) add varied examples
  31. 31.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  32. 32.True or false: “use one group only” helps reduce bias. (a) True (b) False
  33. 33.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
  34. 34.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  35. 35.🔍 Which is the odd one out? (a) fix wrong labels (b) skip testing (c) copy old unfair choices (d) test on one group only
  36. 36.Which of these helps reduce bias? (a) ignore complaints (b) test on many groups (c) use one group only (d) test on one group only
  37. 37.🔍 Which is the odd one out? (a) add more of the same (b) use one group only (c) test on many groups (d) copy old unfair choices
  38. 38.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  39. 39.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
  40. 40.True or false: “add varied examples” helps reduce bias. (a) True (b) False

Answer key

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

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