Leaks: Why the Government Condemns and Condones Them
Leaks of classified information and the government’s responses to them are the subject of a new study by David Pozen of Columbia Law School.
The starting point for his examination is the “dramatic disconnect between the way our laws and our leaders condemn leaking in the abstract and the way they condone it in practice.” How can this disconnect be understood?
Leaks benefit the government, the author argues, in many ways. They are a safety valve, a covert messaging system, a perception management tool, and more. Even when a particular disclosure is unwelcome or damaging, it serves to validate the system as a whole.
This thesis may explain why the number of leak prosecutions is still lower than might be expected, given the prevalence of leaks, and why new legislative proposals to combat leaks have met with a lukewarm response from executive branch officials.
“The leak laws are so rarely enforced not only because it is hard to punish violators, but also because key institutional actors share overlapping interests in maintaining a permissive culture of classified information disclosures.”
The article is full of stimulating observations woven into an original and provocative thesis. See The Leaky Leviathan: Why the Government Condemns and Condones Unlawful Disclosures of Information by David Pozen, to be published in Harvard Law Review.
From grassroots community impacts to global geopolitical dynamics, understanding developing data center capacities is emerging as a critical analytical challenge.
Over the past few months, the Trump administration has been laying the foundation to expand the use of the Defense Production Act (DPA) for energy infrastructure and supply chains.
Get it right, and pooled hiring becomes a model for how the federal government decides what to do together and what to do apart. That’s a bigger prize than faster hiring. It’s a more functional government.
As of March 2026, there were at least nine documented U.S. wrongful arrests tied to face recognition misidentification. Errors like these are as much human as machine.