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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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Mia has shared ways you can use algorithms to your advantage. What are three actions you want to try to improve the overall quality of the search results or content algorithms serve you?

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