Washington Law Review
Margot E. Kaminski, Regulating Real-World Surveillance, 90 Wash. L. Rev. 1113 (2015), available at https://scholar.law.colorado.edu/articles/405.
A number of laws govern information gathering, or surveillance, by private parties in the physical world. But we lack a compelling theory of privacy harm that accounts for the state's interest in enacting these laws. Without a theory of privacy harm, these laws will be enacted piecemeal. Legislators will have a difficult time justifying the laws to constituents; the laws will not be adequately tailored to legislative interest; and courts will find it challenging to weigh privacy harms against other strong values, such as freedom of expression.
This Article identifies the government interest in enacting laws governing surveillance by private parties. Using social psychologist Irwin Altman's framework of "boundary management" as a jumping-off point, I conceptualize privacy harm as interference in an individual's ability to dynamically manage disclosure and social boundaries. Stemming from this understanding of privacy, the government has two related interests in enacting laws prohibiting surveillance: an interest in providing notice so that an individual can adjust her behavior; and an interest in prohibiting surveillance to prevent undesirable behavioral shifts.
Framing the government interest, or interests, this way has several advantages. First, it descriptively maps on to existing laws: These laws either help individuals manage their desired level of disclosure by requiring notice, or prevent individuals from resorting to undesirable behavioral shifts by banning surveillance. Second, the framework helps us assess the strength and legitimacy of the legislative interest in these laws. Third, it allows courts to understand how First Amendment interests are in fact internalized in privacy laws. And fourth, it provides guidance to legislators for the enactment of new laws governing a range of new surveillance technologies-from automated license plate readers (ALPRs) to robots to drones.
Copyright protected. Use of materials from this collection beyond the exceptions provided for in the Fair Use and Educational Use clauses of the U.S. Copyright Law may violate federal law. Permission to publish or reproduce is required.