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Discourse and Research from TechLaw

Privacy Problem Solving

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Focus Areas: Privacy Problem Solving

The Privacy Problem-Solving Project (“the Project”) has a mission to translate what we know so far about the law, economics, and psychology of privacy into concrete business practice guidance and model legislation. Faculty, research fellows, and students who take Prof. Bambauer’s privacy law course form a multidisciplinary team to analyze a problem related to personal data collection and use. We do so with sensitivity to the pragmatic goals and realistic costs and benefits that the problem raises. This work involves translating many of the insights that have come from research in law, economics, and psychology into practical steps, rules-of-thumb, and best practices.

Past and current projects include: 


Discourse Type: Policy Papers
In this essay for Lawfare, Prof. Bambauer describes how Europe’s new Digital Markets Act, which is intended to increase competition among digital services companies, is on a collision course with the GDPR and other privacy laws.
Discourse Type: In the Media
Timothy B. Lee, Why the Census Invented Nine Fake People in One House, Slate (March 2, 2022) (quoting Jane Bambauer)
Discourse Type: In the Media
Cyberlaw Podcast, Episode 398: Scarlett Johannsson Finally Makes an Appearance on the Cyberlaw Podcast (March 14, 2022) (featuring Jane Bambauer as a panelist)
Discourse Type: In the Media
Washington Post (March 28, 2022), and a hit piece in response, published on TechDirt, wherein the author anger-reads nearly all of Jane Bambauer’s Fourth Amendment scholarship
Discourse Type: Books
This acclaimed Economic and Dignitary Torts casebook has been completely revised to include the most up-to-date and comprehensive coverage in the field. The new edition includes hundreds of recent cases that illustrate important contemporary contexts such as the problem of defamation on the Internet; the application of common law privacy
Discourse Type: Policy Papers
This Essay, produced for the Hoover Institute as part of the Aegis series, defends the police use of facial recognition technology to identify suspects in crime footage or to locate individuals with outstanding warrants. It argues that the perils that flow from facial recognition can be mitigated through sensible limits
Discourse Type: Scholarship
Forthcoming in the U.C. Davis Law Review If a machine learning algorithm treats two people very differently because of a slight difference in their attributes, the result intuitively seems unfair. Indeed, an aversion to this sort of treatment has already begun to affect regulatory practices in employment and lending. But

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