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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 645 · 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: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
  2. 2.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  3. 3.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  4. 4.True or false: “test on many groups” adds to bias. (a) False (b) True
  5. 5.🗣️ 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
  6. 6.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  7. 7.Which of these helps reduce bias? (a) test on many groups (b) ignore complaints (c) copy old unfair choices (d) skip testing
  8. 8.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) the weather (c) its screen size (d) its training data
  9. 9.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
  10. 10.Which of these adds to bias? (a) test on many groups (b) balance the data (c) check who is missing (d) ignore complaints
  11. 11.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  12. 12.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) fewer tests (c) one group only (d) more varied data
  13. 13.True or false: “add varied examples” adds to bias. (a) False (b) True
  14. 14.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
  15. 15.🔍 Which is the odd one out? (a) add varied examples (b) copy old unfair choices (c) skip testing (d) use one group only
  16. 16.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  17. 17.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) ignore complaints (d) balance the data
  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: “add varied examples” helps reduce bias. (a) False (b) True
  20. 20.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  21. 21.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  22. 22.True or false: “test on one group only” adds to bias. (a) False (b) True
  23. 23.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  24. 24.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  25. 25.Which of these adds to bias? (a) test on many groups (b) fix wrong labels (c) add more of the same (d) add varied examples
  26. 26.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  27. 27.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  28. 28.True or false: “ask different people” adds to bias. (a) False (b) True
  29. 29.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  30. 30.🔍 Which is the odd one out? (a) balance the data (b) add varied examples (c) check who is missing (d) copy old unfair choices
  31. 31.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  32. 32.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  33. 33.True or false: “add more of the same” helps reduce bias. (a) False (b) True
  34. 34.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a broken screen (c) bias from data (d) a fair model
  35. 35.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  36. 36.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) test on one group only (d) balance the data
  37. 37.Which of these adds to bias? (a) ask different people (b) fix wrong labels (c) balance the data (d) use one group only
  38. 38.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
  39. 39.🔍 Which is the odd one out? (a) skip testing (b) test on many groups (c) test on one group only (d) copy old unfair choices
  40. 40.True or false: “balance the data” adds to bias. (a) False (b) True

Answer key

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

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