← Bias: when data is unfair lesson New set →

AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 43 · 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: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  2. 2.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  3. 3.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  4. 4.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  5. 5.🔍 Which is the odd one out? (a) skip testing (b) ignore complaints (c) add varied examples (d) copy old unfair choices
  6. 6.True or false: “balance the data” helps reduce bias. (a) False (b) True
  7. 7.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its training data (c) its screen size (d) the weather
  8. 8.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) one group only (c) more varied data (d) fewer tests
  9. 9.True or false: “skip testing” adds to bias. (a) False (b) True
  10. 10.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a broken screen (c) bias from data (d) a strong battery
  11. 11.True or false: “check who is missing” adds to bias. (a) False (b) True
  12. 12.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) it was a rainy day (c) children speak too fast (d) the phone was new
  13. 13.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  14. 14.Which of these adds to bias? (a) fix wrong labels (b) ask different people (c) copy old unfair choices (d) test on many groups
  15. 15.Which of these helps reduce bias? (a) copy old unfair choices (b) fix wrong labels (c) add more of the same (d) ignore complaints
  16. 16.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
  17. 17.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
  18. 18.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  19. 19.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  20. 20.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  21. 21.🔍 Which is the odd one out? (a) check who is missing (b) ignore complaints (c) ask different people (d) balance the data
  22. 22.🔍 Which is the odd one out? (a) ignore complaints (b) skip testing (c) ask different people (d) use one group only
  23. 23.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) False (b) True
  24. 24.🔍 Which is the odd one out? (a) check who is missing (b) add more of the same (c) test on many groups (d) add varied examples
  25. 25.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  26. 26.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  27. 27.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  28. 28.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  29. 29.True or false: “skip testing” helps reduce bias. (a) False (b) True
  30. 30.Which of these adds to bias? (a) test on many groups (b) fix wrong labels (c) test on one group only (d) add varied examples
  31. 31.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  32. 32.True or false: “balance the data” adds to bias. (a) True (b) False
  33. 33.Which of these adds to bias? (a) skip testing (b) test on many groups (c) add varied examples (d) ask different people
  34. 34.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  35. 35.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  36. 36.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  37. 37.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  38. 38.True or false: “add varied examples” adds to bias. (a) True (b) False
  39. 39.Which of these adds to bias? (a) ignore complaints (b) test on many groups (c) fix wrong labels (d) check who is missing
  40. 40.True or false: “ask different people” adds to bias. (a) True (b) False

Answer key

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

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