← Bias: when data is unfair lesson New set →

AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 108 · 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: “add varied examples” adds to bias. (a) True (b) False
  2. 2.True or false: “test on one group only” adds to bias. (a) True (b) False
  3. 3.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its colour (c) the weather (d) its screen size
  4. 4.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  5. 5.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  6. 6.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  7. 7.🔍 Which is the odd one out? (a) ask different people (b) add more of the same (c) skip testing (d) use one group only
  8. 8.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  9. 9.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  10. 10.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) test on one group only (d) fix wrong labels
  11. 11.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  12. 12.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  13. 13.True or false: “check who is missing” adds to bias. (a) True (b) False
  14. 14.True or false: “check who is missing” helps reduce bias. (a) True (b) False
  15. 15.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  16. 16.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  17. 17.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) False (b) True
  18. 18.🔍 Which is the odd one out? (a) use one group only (b) add varied examples (c) balance the data (d) ask different people
  19. 19.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  20. 20.True or false: “use one group only” helps reduce bias. (a) True (b) False
  21. 21.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) the phone was new (c) few child voices in data (d) children speak too fast
  22. 22.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
  23. 23.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  24. 24.Which of these adds to bias? (a) skip testing (b) fix wrong labels (c) balance the data (d) add varied examples
  25. 25.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  26. 26.🔍 Which is the odd one out? (a) ignore complaints (b) copy old unfair choices (c) balance the data (d) skip testing
  27. 27.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  28. 28.Which of these helps reduce bias? (a) skip testing (b) use one group only (c) add varied examples (d) add more of the same
  29. 29.True or false: “add more of the same” helps reduce bias. (a) False (b) True
  30. 30.🔍 Which is the odd one out? (a) add varied examples (b) copy old unfair choices (c) ignore complaints (d) use one group only
  31. 31.True or false: “ask different people” adds to bias. (a) False (b) True
  32. 32.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  33. 33.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  34. 34.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
  35. 35.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  36. 36.🔍 Which is the odd one out? (a) fix wrong labels (b) check who is missing (c) test on many groups (d) ignore complaints
  37. 37.True or false: “add more of the same” adds to bias. (a) True (b) False
  38. 38.Which of these adds to bias? (a) copy old unfair choices (b) balance the data (c) add varied examples (d) test on many groups
  39. 39.Which of these adds to bias? (a) test on many groups (b) balance the data (c) add varied examples (d) add more of the same
  40. 40.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True

Answer key

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

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