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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 102 · 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) add more of the same (d) check who is missing
  2. 2.🔍 Which is the odd one out? (a) check who is missing (b) test on many groups (c) add varied examples (d) copy old unfair choices
  3. 3.Which of these adds to bias? (a) ask different people (b) check who is missing (c) balance the data (d) ignore complaints
  4. 4.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  5. 5.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  6. 6.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  7. 7.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  8. 8.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  9. 9.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  10. 10.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) True (b) False
  11. 11.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  12. 12.Which of these helps reduce bias? (a) test on one group only (b) copy old unfair choices (c) balance the data (d) ignore complaints
  13. 13.Which of these helps reduce bias? (a) use one group only (b) test on one group only (c) fix wrong labels (d) ignore complaints
  14. 14.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) it was a rainy day (c) children speak too fast (d) few child voices in data
  15. 15.True or false: “add varied examples” adds to bias. (a) True (b) False
  16. 16.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
  17. 17.🛠️ What helps make an AI system fairer? (a) one group only (b) fewer tests (c) ignoring mistakes (d) more varied data
  18. 18.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  19. 19.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  20. 20.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) True (b) False
  21. 21.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
  22. 22.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
  23. 23.True or false: “balance the data” adds to bias. (a) False (b) True
  24. 24.🔍 Which is the odd one out? (a) add more of the same (b) check who is missing (c) test on one group only (d) ignore complaints
  25. 25.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its training data (c) its screen size (d) the weather
  26. 26.True or false: “check who is missing” adds to bias. (a) False (b) True
  27. 27.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  28. 28.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a fair model (c) a strong battery (d) bias from data
  29. 29.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
  30. 30.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  31. 31.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  32. 32.🔍 Which is the odd one out? (a) skip testing (b) ask different people (c) test on one group only (d) copy old unfair choices
  33. 33.🔍 Which is the odd one out? (a) test on one group only (b) skip testing (c) add varied examples (d) ignore complaints
  34. 34.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  35. 35.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  36. 36.True or false: “add more of the same” adds to bias. (a) False (b) True
  37. 37.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  38. 38.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  39. 39.Which of these adds to bias? (a) check who is missing (b) balance the data (c) add varied examples (d) skip testing
  40. 40.True or false: “copy old unfair choices” adds to bias. (a) True (b) False

Answer key

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

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