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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 196 · 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: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  2. 2.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  3. 3.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  4. 4.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  5. 5.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  6. 6.🔍 Which is the odd one out? (a) copy old unfair choices (b) add varied examples (c) balance the data (d) ask different people
  7. 7.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) it was a rainy day (c) few child voices in data (d) the phone was new
  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.🔍 Which is the odd one out? (a) add more of the same (b) skip testing (c) ignore complaints (d) ask different people
  10. 10.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  11. 11.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  12. 12.Which of these adds to bias? (a) ask different people (b) add varied examples (c) test on one group only (d) test on many groups
  13. 13.🔍 Which is the odd one out? (a) add varied examples (b) skip testing (c) copy old unfair choices (d) add more of the same
  14. 14.True or false: “ask different people” adds to bias. (a) True (b) False
  15. 15.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  16. 16.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a broken screen (c) a fair model (d) bias from data
  17. 17.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
  18. 18.🔍 Which is the odd one out? (a) ignore complaints (b) check who is missing (c) use one group only (d) test on one group only
  19. 19.True or false: “test on many groups” adds to bias. (a) False (b) True
  20. 20.True or false: “use one group only” helps reduce bias. (a) False (b) True
  21. 21.🛠️ What helps make an AI system fairer? (a) one group only (b) more varied data (c) ignoring mistakes (d) fewer tests
  22. 22.Which of these helps reduce bias? (a) copy old unfair choices (b) skip testing (c) add varied examples (d) test on one group only
  23. 23.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  24. 24.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
  25. 25.True or false: “skip testing” adds to bias. (a) False (b) True
  26. 26.Which of these adds to bias? (a) check who is missing (b) ask different people (c) add varied examples (d) add more of the same
  27. 27.Which of these helps reduce bias? (a) copy old unfair choices (b) use one group only (c) check who is missing (d) ignore complaints
  28. 28.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  29. 29.🔍 Which is the odd one out? (a) use one group only (b) skip testing (c) test on many groups (d) copy old unfair choices
  30. 30.Which of these adds to bias? (a) add varied examples (b) use one group only (c) ask different people (d) balance the data
  31. 31.True or false: “add varied examples” adds to bias. (a) False (b) True
  32. 32.🔍 Which is the odd one out? (a) skip testing (b) test on one group only (c) balance the data (d) add more of the same
  33. 33.🔍 Which is the odd one out? (a) balance the data (b) test on one group only (c) test on many groups (d) fix wrong labels
  34. 34.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
  35. 35.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  36. 36.True or false: “use one group only” adds to bias. (a) True (b) False
  37. 37.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
  38. 38.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  39. 39.🔍 Which is the odd one out? (a) add varied examples (b) balance the data (c) check who is missing (d) skip testing
  40. 40.🔍 Which is the odd one out? (a) fix wrong labels (b) add more of the same (c) balance the data (d) test on many groups

Answer key

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

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