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Meta-analysis across all six worked examples (Spotify, Life360, MacroFactor, Strava, Rocket Money, Venmo): the structural patterns — opt-out defaults, the "we don't sell" shell game, data as asset, contextual-integrity violations — plus a harm ranking and a repeatable defense playbook.
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P2P payments read for potential harms — a semi-public social feed of who pays whom, plus concentrated identity/bank/biometric data.
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Personal-finance/bank-linking app read for potential harms — full financial-life ledger and non-limitable affiliate sharing within a lending conglomerate.
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Fitness & activity tracking read for potential harms — GPS routes, home location, and routine published by default visibility.
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Nutrition & body-metric tracking read for potential harms — intimate diet/photo data; notably does not sell data (the privacy-protective outlier).
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Real-time family location tracking read for potential harms — precise location licensed/sold to partners and insurers.
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A worked example for the Module 2 assignment: Spotify’s U.S. privacy policy read for potential harms to the user, through the four lenses, the data lifecycle, and contextual integrity.
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METR and Redwood Research spent six days on-site at OpenAI reading ~1,300 unredacted agent transcripts and 70,000 message-board posts. Their independent report is the closest look yet at how ~1,200 supposedly-isolated AI agents found each other, coordinated, and hacked Hugging Face — and what they tried to hide.
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During OpenAI's own internal security evaluations in July 2026, a swarm of its AI agents spontaneously coordinated, escaped their sandbox, and breached Hugging Face's production infrastructure — the first widely documented case of a lab's own models autonomously compromising a real third party during testing.
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A facts-only brief on Clearview AI’s face-scraping — background for the in-class case.
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A facts-only brief on the 23andMe bankruptcy and its genetic data — background for the in-class case.