← Bias: when data is unfair lesson New set →

AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 879 · 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: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
  2. 2.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  3. 3.🔍 Which is the odd one out? (a) add varied examples (b) copy old unfair choices (c) check who is missing (d) balance the data
  4. 4.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  5. 5.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  6. 6.🛠️ What helps make an AI system fairer? (a) more varied data (b) one group only (c) ignoring mistakes (d) fewer tests
  7. 7.🔍 Which is the odd one out? (a) copy old unfair choices (b) use one group only (c) test on one group only (d) test on many groups
  8. 8.True or false: “use one group only” adds to bias. (a) True (b) False
  9. 9.🔍 Which is the odd one out? (a) use one group only (b) check who is missing (c) skip testing (d) copy old unfair choices
  10. 10.Which of these adds to bias? (a) add varied examples (b) test on one group only (c) test on many groups (d) check who is missing
  11. 11.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
  12. 12.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) few child voices in data (c) it was a rainy day (d) the phone was new
  13. 13.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  14. 14.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  15. 15.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) the weather (c) its screen size (d) its training data
  16. 16.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
  17. 17.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
  18. 18.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  19. 19.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  20. 20.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  21. 21.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  22. 22.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  23. 23.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
  24. 24.Which of these adds to bias? (a) ask different people (b) balance the data (c) skip testing (d) fix wrong labels
  25. 25.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  26. 26.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
  27. 27.Which of these helps reduce bias? (a) skip testing (b) test on many groups (c) ignore complaints (d) use one group only
  28. 28.Which of these helps reduce bias? (a) add more of the same (b) ask different people (c) skip testing (d) use one group only
  29. 29.True or false: “ignore complaints” adds to bias. (a) True (b) False
  30. 30.🔍 Which is the odd one out? (a) skip testing (b) copy old unfair choices (c) test on one group only (d) balance the data
  31. 31.True or false: “check who is missing” adds to bias. (a) True (b) False
  32. 32.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  33. 33.True or false: “test on one group only” helps reduce bias. (a) False (b) True
  34. 34.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  35. 35.True or false: “balance the data” adds to bias. (a) True (b) False
  36. 36.Which of these helps reduce bias? (a) check who is missing (b) add more of the same (c) copy old unfair choices (d) test on one group only
  37. 37.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  38. 38.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  39. 39.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  40. 40.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias

Answer key

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

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