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  1. Introduction to Algorithms
  2. Video 1: Open
  3. List experiences
  4. Ch 2 title
  5. Video 2: Algos are like recipes
  6. What are algos? (10 points)
  7. Video 3: Machine learning
  8. What is training data? (10 points)
  9. Video 4: Black box problem
  10. Explain training data and bias (25 points)
  11. Video 5: How algos work
  12. How do search algos work? (10 points)
  13. Video 6: Bias in search algos
  14. Which search least biased? (10 points)
  15. Ch 3 title
  16. Video 7: The attention economy
  17. Reorder attention economy (10 points)
  18. Video 8: Engagement
  19. Costs of attention economy (10 points)
  20. Explain two-way process (25 points)
  21. Ch 4 title
  22. Video 9: Celebrity clones
  23. What can you do to reduce? (10 points)
  24. Video 10: Filter bubbles
  25. Match filter bubble and rabbit hole (10 points)
  26. Feedback, filter, rabbit (25 points)
  27. Video 11: Amplifying bias
  28. Bias from training data (10 points)
  29. Ch 5 title
  30. Video 12: Introducing Gen
  31. Why gen inaccuracies? (10 points)
  32. Video 13: AI image generators
  33. Why AI images stereotypes? (10 points)
  34. Video 14: Other generative AI developments
  35. AI image feedback algo bias (10 points)
  36. Argue the risks and benefits (25 points)
  37. Ch 6 title
  38. Video 15 conclusion
  39. List ways algo advantage
  40. If you were asked to make a plan (10 points)
  41. Acknowledgments
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