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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 865 · 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: ask different people? (a) reduces bias (b) adds to bias
  2. 2.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
  3. 3.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  4. 4.Which of these helps reduce bias? (a) ignore complaints (b) test on one group only (c) test on many groups (d) copy old unfair choices
  5. 5.True or false: “skip testing” helps reduce bias. (a) False (b) True
  6. 6.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) children speak too fast (c) few child voices in data (d) the phone was new
  7. 7.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its training data (d) its screen size
  8. 8.Which of these adds to bias? (a) balance the data (b) add varied examples (c) copy old unfair choices (d) check who is missing
  9. 9.🖼️ 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
  10. 10.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  11. 11.🔍 Which is the odd one out? (a) fix wrong labels (b) add more of the same (c) test on many groups (d) check who is missing
  12. 12.Which of these adds to bias? (a) test on many groups (b) add varied examples (c) test on one group only (d) check who is missing
  13. 13.🔍 Which is the odd one out? (a) ignore complaints (b) ask different people (c) test on one group only (d) skip testing
  14. 14.🔍 Which is the odd one out? (a) balance the data (b) add varied examples (c) check who is missing (d) ignore complaints
  15. 15.True or false: “use one group only” helps reduce bias. (a) True (b) False
  16. 16.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  17. 17.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  18. 18.🔍 Which is the odd one out? (a) add varied examples (b) use one group only (c) ignore complaints (d) skip testing
  19. 19.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
  20. 20.🛠️ What helps make an AI system fairer? (a) one group only (b) more varied data (c) fewer tests (d) ignoring mistakes
  21. 21.🔍 Which is the odd one out? (a) check who is missing (b) ask different people (c) test on one group only (d) add varied examples
  22. 22.True or false: “use one group only” adds to bias. (a) True (b) False
  23. 23.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  24. 24.True or false: “add varied examples” helps reduce bias. (a) False (b) True
  25. 25.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  26. 26.Which of these adds to bias? (a) ask different people (b) add varied examples (c) ignore complaints (d) fix wrong labels
  27. 27.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  28. 28.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  29. 29.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) False (b) True
  30. 30.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  31. 31.🔍 Which is the odd one out? (a) test on many groups (b) copy old unfair choices (c) add varied examples (d) ask different people
  32. 32.True or false: “check who is missing” adds to bias. (a) True (b) False
  33. 33.True or false: “test on many groups” adds to bias. (a) False (b) True
  34. 34.Which of these helps reduce bias? (a) test on one group only (b) skip testing (c) check who is missing (d) add more of the same
  35. 35.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  36. 36.Which of these helps reduce bias? (a) skip testing (b) balance the data (c) use one group only (d) add more of the same
  37. 37.Which of these helps reduce bias? (a) ignore complaints (b) fix wrong labels (c) copy old unfair choices (d) add more of the same
  38. 38.Which of these helps reduce bias? (a) copy old unfair choices (b) ignore complaints (c) skip testing (d) add varied examples
  39. 39.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) skip testing (d) fix wrong labels
  40. 40.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data

Answer key

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

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