← Bias: when data is unfair lesson New set →

AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 173 · 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 voice app trained only on adult voices? (a) balanced data (b) one-sided data
  2. 2.🔍 Which is the odd one out? (a) add varied examples (b) copy old unfair choices (c) test on many groups (d) check who is missing
  3. 3.True or false: “use one group only” adds to bias. (a) False (b) True
  4. 4.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  5. 5.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  6. 6.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  7. 7.Which of these helps reduce bias? (a) test on one group only (b) skip testing (c) use one group only (d) fix wrong labels
  8. 8.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) True (b) False
  9. 9.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  10. 10.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
  11. 11.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  12. 12.🔍 Which is the odd one out? (a) fix wrong labels (b) add more of the same (c) check who is missing (d) test on many groups
  13. 13.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  14. 14.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  15. 15.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  16. 16.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  17. 17.🔍 Which is the odd one out? (a) ask different people (b) check who is missing (c) balance the data (d) skip testing
  18. 18.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  19. 19.True or false: “copy old unfair choices” helps reduce bias. (a) False (b) True
  20. 20.Which of these adds to bias? (a) fix wrong labels (b) ask different people (c) add varied examples (d) copy old unfair choices
  21. 21.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its screen size (d) its training data
  22. 22.🔍 Which is the odd one out? (a) use one group only (b) ask different people (c) copy old unfair choices (d) test on one group only
  23. 23.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  24. 24.🔍 Which is the odd one out? (a) balance the data (b) add varied examples (c) use one group only (d) check who is missing
  25. 25.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  26. 26.🔍 Which is the odd one out? (a) add more of the same (b) use one group only (c) balance the data (d) ignore complaints
  27. 27.True or false: “balance the data” helps reduce bias. (a) True (b) False
  28. 28.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  29. 29.True or false: “test on many groups” adds to bias. (a) True (b) False
  30. 30.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  31. 31.🔍 Which is the odd one out? (a) ignore complaints (b) add varied examples (c) add more of the same (d) skip testing
  32. 32.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
  33. 33.True or false: “add more of the same” adds to bias. (a) False (b) True
  34. 34.Which of these helps reduce bias? (a) check who is missing (b) skip testing (c) copy old unfair choices (d) add more of the same
  35. 35.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  36. 36.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  37. 37.Which of these helps reduce bias? (a) use one group only (b) test on many groups (c) copy old unfair choices (d) ignore complaints
  38. 38.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  39. 39.🔍 Which is the odd one out? (a) fix wrong labels (b) test on one group only (c) balance the data (d) check who is missing
  40. 40.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data

Answer key

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

Free ai for students lessons, practice and worksheets at talentjr.in/ai-for-students