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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
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Which of the following are examples of algorithmic bias that stem from flawed training data?
Select the 2 that apply
Algorithms designed to learn and recognize human faces are trained using photos of mostly white faces.
Social media algorithms suggest new accounts to follow based on shared interests.
Search engine suggestion algorithms learn sexist associations from people’s search patterns.
Algorithms designed to predict shopping patterns are trained on actual sales data.
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