More on the “Incomprehensible” Espionage Act of 1917
The Espionage Act is “in many respects incomprehensible,” wrote Harold Edgar and Benno C. Schmidt, Jr. in a definitive law review article (large pdf) three decades ago which explored the potential use of the Act to prosecute leaks to the media.
The espionage statutes are “so sweeping as to be absurd,” they argued (previously noted in Secrecy News, 10/19/05).
“If these statutes mean what they seem to say and are constitutional, public speech in this country since World War II has been rife with criminality.”
Now a scan of that 1973 paper is available online.
See “The Espionage Statutes and Publication of Defense Information,” Columbia Law Review, May 1973, vol. 73, pp. 929-1087 (a large 6.3 MB PDF file).
Though it remains the best account of the legislative history of the Espionage Act, the Edgar/Schmidt article is not the last or the latest word on the meaning of the Act. In particular, the prosecution of Samuel L. Morison in 1985 for providing classified satellite photos to Jane’s Defence Weekly established that the Espionage Act could be used to successfully prosecute leakers.
An article in the current issue of Commentary Magazine now calls for the prosecution of the New York Times for disclosing the NSA warrantless surveillance activity.
Though many experts consider the NSA program to be illegal because it violates the clear language of the Foreign Intelligence Surveillance Act, Commentary author Gabriel Schoenfeld argues that disclosure of the program is the crime that should be investigated and prosecuted.
That perspective is examined in “Bill Keller in Chains: Commentary’s case for prosecuting the Times under the Espionage Act” by Jack Shafer, Slate, March 9.
With thoughtful policy action, it is still possible to build systems that are fair, transparent, and accountable, and to earn the public trust that will ultimately determine AI’s future. We hope policymakers are ready to act.
Procurement is not merely an administrative function—it is how AI enters government and the first line of defense for responsible AI in the public sector.
Responsible AI starts with who is in the data, who is at the table, whose needs shape the outcome, and who is responsible when it falls short.
There is no question this is a Big Deal. If you are a university or research lab, or aspire to work in one, or are simply an enthusiast of federally-funded research, what’s next will matter.