Margot Kaminski

Margot E. Kaminski is an American professor who works at the intersection of artificial intelligence, privacy, information governance, and online civil liberties. She is currently an Associate Professor at the University of Colorado Law School and the Director of Privacy Initiative at the Silicon Flatirons Center for Law, Technology, and Entrepreneurship. Her research examines the impacts of new technologies, including autonomous systems, on individual rights to help shape policy and regulation of AI. Quotes 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. The newly developing “law of AI” has come to focus on risk regulation, and in many ways risk regulation seems like a good fit for regulating the development and growing uses of AI systems. AI harms tend to be systemic, occur at scale, raise causality challenges for potential litigators, and may not yet be vested (that is, they may constitute risks of future harm rather than current harm)—all challenges for traditional liability regimes. But as I argue in this paper, risk regulation also comes with what I call “policy baggage”: known problems that have emerged in other fields. Choosing to use risk regulation itself entails making a significant normative choice: to develop and use AI systems in the first place rather than adopt more precautionary approaches to AI. Risk regulation thus embodies what Jessica Eaglin has called a “techno-correctionist” tendency prevalent in scholarship on AI systems: the tendency to try to make technology “better” rather than to question the politics and appropriateness of its usage and to explore more systematically whether, given its harms, it should be used at all. Regulators should broaden their regulatory toolkit and move away from, or at least add to, the current narrow focus on AI impact assessments. If regulators want to truly address the harms caused by AI systems, they are going to have to do better than light-touch risk regulation. [ External link Category: living people Category:Women academics from the United States Category:Harvard University alumni Category:Yale University alumni Category:Yale University faculty