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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 142 · 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: balance the data? (a) reduces bias (b) adds to bias
  2. 2.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
  3. 3.Which of these helps reduce bias? (a) test on one group only (b) ignore complaints (c) ask different people (d) copy old unfair choices
  4. 4.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  5. 5.True or false: “test on many groups” adds to bias. (a) True (b) False
  6. 6.Which of these helps reduce bias? (a) add more of the same (b) use one group only (c) copy old unfair choices (d) fix wrong labels
  7. 7.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  8. 8.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  9. 9.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  10. 10.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  11. 11.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
  12. 12.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  13. 13.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a fair model (c) bias from data (d) a strong battery
  14. 14.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  15. 15.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  16. 16.🔍 Which is the odd one out? (a) fix wrong labels (b) add varied examples (c) copy old unfair choices (d) test on many groups
  17. 17.🔍 Which is the odd one out? (a) add varied examples (b) test on one group only (c) check who is missing (d) fix wrong labels
  18. 18.Which of these adds to bias? (a) add varied examples (b) copy old unfair choices (c) ask different people (d) test on many groups
  19. 19.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its training data (d) its screen size
  20. 20.Which of these helps reduce bias? (a) add more of the same (b) check who is missing (c) ignore complaints (d) skip testing
  21. 21.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  22. 22.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  23. 23.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  24. 24.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  25. 25.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  26. 26.🔍 Which is the odd one out? (a) skip testing (b) fix wrong labels (c) balance the data (d) ask different people
  27. 27.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  28. 28.🔍 Which is the odd one out? (a) check who is missing (b) add varied examples (c) add more of the same (d) fix wrong labels
  29. 29.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
  30. 30.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
  31. 31.True or false: “check who is missing” adds to bias. (a) True (b) False
  32. 32.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False
  33. 33.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  34. 34.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
  35. 35.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) True (b) False
  36. 36.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  37. 37.🗣️ 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
  38. 38.True or false: “ignore complaints” adds to bias. (a) False (b) True
  39. 39.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  40. 40.True or false: “balance the data” helps reduce bias. (a) True (b) False

Answer key

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

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