Natalie Ram is assistant professor of Law at University of Baltimore School of Law. Her primary research and teaching interests are in biotechnology and the law, bioethics, and innovation policy. Professor Ram is also the associate director of UB’s Center for Medicine and Law. Professor Ram’s recent scholarship has appeared or is forthcoming in the Northwestern University Law Review, Harvard Law Review, Columbia Law Review, Stanford Law Review, and Iowa Law Review, among others. She has also published in Slate and appeared on Here & Now and Science Friday.

Professor Ram earned an A.B. in public and international affairs from the Woodrow Wilson School at Princeton University. She received her J.D. from Yale Law School. After law school, Ram clerked for Judge Guido Calabresi of the U.S. Court of Appeals for the Second Circuit and then for Justice Stephen G. Breyer of the Supreme Court of the United States. Ram also served as a Greenwall fellow in bioethics and practiced law at a firm in Washington, D.C.

Talk: "Rebuilding Privacy Practices After Carpenter"

Abstract: The recent arrest of the alleged Golden State Killer has ignited law enforcement interest in using genetic genealogy databases to crack cold cases. The break in that case came when investigators compared DNA recovered from crime scenes to other DNA profiles searchable in an online genetic genealogy database called GEDmatch. Yet, genealogical genetic services have responded to increased law enforcement interest in markedly different ways. GEDmatch updated its user interface, terms of service, and privacy policy to welcome law enforcement expressly, authorizing law enforcement to use its platform to investigate “violent crimes.” 23andMe and AncestryDNA, by contrast, have emphasized their commitment “to resist law enforcement inquiries to protect customer privacy.” At almost the same time, the Supreme Court gave these platforms a reinvigorated role in policing police access to their genetic resources. In Carpenter v. United States, the Court upended the previously categorical rule that one cannot have an expectation of privacy in data shared with another.