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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 85 · 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: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  2. 2.Which of these helps reduce bias? (a) use one group only (b) add more of the same (c) check who is missing (d) test on one group only
  3. 3.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  4. 4.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  5. 5.🗣️ 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
  6. 6.🔍 Which is the odd one out? (a) check who is missing (b) fix wrong labels (c) skip testing (d) test on many groups
  7. 7.🔍 Which is the odd one out? (a) test on many groups (b) balance the data (c) test on one group only (d) fix wrong labels
  8. 8.Which of these adds to bias? (a) fix wrong labels (b) ask different people (c) balance the data (d) skip testing
  9. 9.Which of these helps reduce bias? (a) test on one group only (b) fix wrong labels (c) copy old unfair choices (d) use one group only
  10. 10.Which of these helps reduce bias? (a) add varied examples (b) use one group only (c) copy old unfair choices (d) add more of the same
  11. 11.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  12. 12.Which of these adds to bias? (a) test on one group only (b) ask different people (c) check who is missing (d) balance the data
  13. 13.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  14. 14.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  15. 15.True or false: “copy old unfair choices” adds to bias. (a) False (b) True
  16. 16.🔍 Which is the odd one out? (a) test on many groups (b) test on one group only (c) use one group only (d) ignore complaints
  17. 17.True or false: “test on many groups” adds to bias. (a) True (b) False
  18. 18.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) its colour (d) the weather
  19. 19.Which of these helps reduce bias? (a) test on one group only (b) skip testing (c) balance the data (d) copy old unfair choices
  20. 20.True or false: “skip testing” helps reduce bias. (a) True (b) False
  21. 21.🛠️ What helps make an AI system fairer? (a) one group only (b) fewer tests (c) more varied data (d) ignoring mistakes
  22. 22.True or false: “skip testing” adds to bias. (a) False (b) True
  23. 23.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a strong battery (c) a fair model (d) bias from data
  24. 24.Which of these helps reduce bias? (a) ignore complaints (b) skip testing (c) use one group only (d) test on many groups
  25. 25.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  26. 26.🔍 Which is the odd one out? (a) copy old unfair choices (b) add varied examples (c) fix wrong labels (d) check who is missing
  27. 27.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  28. 28.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  29. 29.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  30. 30.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  31. 31.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  32. 32.True or false: “use one group only” helps reduce bias. (a) False (b) True
  33. 33.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  34. 34.True or false: “add varied examples” adds to bias. (a) False (b) True
  35. 35.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  36. 36.True or false: “ask different people” adds to bias. (a) True (b) False
  37. 37.🔍 Which is the odd one out? (a) add more of the same (b) fix wrong labels (c) use one group only (d) test on one group only
  38. 38.True or false: “add more of the same” adds to bias. (a) False (b) True
  39. 39.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  40. 40.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data

Answer key

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

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