Exit
Introduction to Algorithms
Video 1: Open
List experiences
Ch 2 title
Video 2: Algos are like recipes
What are algos? (10 points)
Video 3: Machine learning
What is training data? (10 points)
Video 4: Black box problem
Explain training data and bias (25 points)
Video 5: How algos work
How do search algos work? (10 points)
Video 6: Bias in search algos
Which search least biased? (10 points)
Ch 3 title
Video 7: The attention economy
Reorder attention economy (10 points)
Video 8: Engagement
Costs of attention economy (10 points)
Explain two-way process (25 points)
Ch 4 title
Video 9: Celebrity clones
What can you do to reduce? (10 points)
Video 10: Filter bubbles
Match filter bubble and rabbit hole (10 points)
Feedback, filter, rabbit (25 points)
Video 11: Amplifying bias
Bias from training data (10 points)
Ch 5 title
Video 12: Introducing Gen
Why gen inaccuracies? (10 points)
Video 13: AI image generators
Why AI images stereotypes? (10 points)
Video 14: Other generative AI developments
AI image feedback algo bias (10 points)
Argue the risks and benefits (25 points)
Ch 6 title
Video 15 conclusion
List ways algo advantage
If you were asked to make a plan (10 points)
Acknowledgments
25
of
41
Previous
Next
Filter bubbles and rabbit holes are two examples of feedback loops.
Click and drag the white boxes to match the scenario with the correct type.
Rabbit hole
Filter bubble
An algorithm notices someone's interest in health and wellness, then begins to suggest posts about “alternative” medicine. Because the person also engages with these posts, the algorithm sugg
A social media algorithm learns what a person likes, and suggests more of that content. The person engages with these suggested posts, so the algorithm suggests even more of those kinds of po
Copy the link below to share this lesson with others.
Copy and Close