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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 612 · 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.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  2. 2.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  3. 3.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) few child voices in data (c) the phone was new (d) it was a rainy day
  4. 4.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  5. 5.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  6. 6.🛠️ What helps make an AI system fairer? (a) more varied data (b) one group only (c) ignoring mistakes (d) fewer tests
  7. 7.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  8. 8.🔍 Which is the odd one out? (a) add more of the same (b) add varied examples (c) ask different people (d) test on many groups
  9. 9.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  10. 10.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a strong battery (c) bias from data (d) a fair model
  11. 11.True or false: “ignore complaints” adds to bias. (a) False (b) True
  12. 12.Which of these helps reduce bias? (a) test on many groups (b) ignore complaints (c) test on one group only (d) copy old unfair choices
  13. 13.True or false: “use one group only” helps reduce bias. (a) False (b) True
  14. 14.Which of these adds to bias? (a) add varied examples (b) test on many groups (c) ignore complaints (d) check who is missing
  15. 15.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its training data (d) its screen size
  16. 16.Which of these adds to bias? (a) test on one group only (b) check who is missing (c) balance the data (d) test on many groups
  17. 17.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
  18. 18.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  19. 19.True or false: “skip testing” helps reduce bias. (a) True (b) False
  20. 20.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  21. 21.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
  22. 22.🔍 Which is the odd one out? (a) test on one group only (b) test on many groups (c) ask different people (d) add varied examples
  23. 23.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  24. 24.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  25. 25.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  26. 26.True or false: “test on many groups” adds to bias. (a) False (b) True
  27. 27.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  28. 28.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  29. 29.True or false: “add varied examples” adds to bias. (a) True (b) False
  30. 30.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
  31. 31.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) True (b) False
  32. 32.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  33. 33.True or false: “ask different people” adds to bias. (a) True (b) False
  34. 34.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  35. 35.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  36. 36.🔍 Which is the odd one out? (a) check who is missing (b) copy old unfair choices (c) add more of the same (d) use one group only
  37. 37.🔍 Which is the odd one out? (a) test on many groups (b) copy old unfair choices (c) skip testing (d) add more of the same
  38. 38.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  39. 39.Which of these adds to bias? (a) test on many groups (b) copy old unfair choices (c) balance the data (d) add varied examples
  40. 40.🔍 Which is the odd one out? (a) check who is missing (b) skip testing (c) test on many groups (d) add varied examples

Answer key

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

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