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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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Based on what you’ve learned in this lesson, which of these is a likely explanation for why AI image generators reproduce harmful stereotypes?
AI image generators learn problematic associations from their training data.
The people who make AI image generators don’t care about racism.
No one has any idea how this happens.
AI image generators only produce stereotypical associations if people ask them to.
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