← Bias: when data is unfair lesson New set →

AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 898 · 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.Which of these adds to bias? (a) ask different people (b) add varied examples (c) check who is missing (d) ignore complaints
  2. 2.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) False (b) True
  3. 3.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  4. 4.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a fair model (c) a broken screen (d) bias from data
  5. 5.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  6. 6.True or false: “test on many groups” adds to bias. (a) True (b) False
  7. 7.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  8. 8.True or false: “balance the data” helps reduce bias. (a) True (b) False
  9. 9.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  10. 10.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  11. 11.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its screen size (c) the weather (d) its colour
  12. 12.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  13. 13.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  14. 14.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  15. 15.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  16. 16.🔍 Which is the odd one out? (a) use one group only (b) add varied examples (c) balance the data (d) test on many groups
  17. 17.True or false: “copy old unfair choices” helps reduce bias. (a) False (b) True
  18. 18.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  19. 19.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  20. 20.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  21. 21.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  22. 22.Which of these helps reduce bias? (a) copy old unfair choices (b) check who is missing (c) use one group only (d) skip testing
  23. 23.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  24. 24.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  25. 25.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) False (b) True
  26. 26.Which of these helps reduce bias? (a) ignore complaints (b) use one group only (c) fix wrong labels (d) copy old unfair choices
  27. 27.Which of these helps reduce bias? (a) skip testing (b) test on many groups (c) copy old unfair choices (d) use one group only
  28. 28.🗣️ 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
  29. 29.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  30. 30.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  31. 31.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  32. 32.🔍 Which is the odd one out? (a) test on many groups (b) skip testing (c) ignore complaints (d) use one group only
  33. 33.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
  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.🛠️ What helps make an AI system fairer? (a) fewer tests (b) ignoring mistakes (c) more varied data (d) one group only
  36. 36.True or false: “ignore complaints” adds to bias. (a) False (b) True
  37. 37.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  38. 38.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
  39. 39.True or false: “ask different people” adds to bias. (a) False (b) True
  40. 40.True or false: “check who is missing” adds to bias. (a) False (b) True

Answer key

  1. ignore complaints
  2. True
  3. False
  4. bias from data
  5. False
  6. False
  7. balanced data
  8. True
  9. balanced data
  10. reduces bias
  11. its training data
  12. False
  13. True
  14. False
  15. reduces bias
  16. use one group only
  17. False
  18. balanced data
  19. one-sided data
  20. adds to bias
  21. False
  22. check who is missing
  23. reduces bias
  24. adds to bias
  25. False
  26. fix wrong labels
  27. test on many groups
  28. few child voices in data
  29. one-sided data
  30. one-sided data
  31. False
  32. test on many groups
  33. True
  34. False
  35. more varied data
  36. True
  37. one-sided data
  38. True
  39. False
  40. False

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