Could the National Security Archive be prosecuted under the Espionage Act for publishing historical documents that U.S. intelligence agencies now say are classified?
Could Secretary of State Condoleezza Rice be detained for continuing to publish historical intelligence records on the State Department web site that the CIA has flagged as classified?
Could thousands of historians and librarians around the country be arrested for retaining and circulating volumes of the State Department’s Foreign Relations of the United States (FRUS) series that are now considered to contain classified documents?
These seem to be silly questions.
And yet the theory of the Espionage Act that has been adopted by the government in its prosecution of two former officials of the American Israel Public Affairs Committee (who are not charged with espionage) may extend even to silly cases such as these.
The Espionage Act’s prohibitions on the unauthorized retention and transmission of national defense information apply to “whoever” may violate them, the government insisted in a January 30 motion.
“Whoever means, ‘no matter who’,” the government contended. “The statute covers ‘anyone’.”
Until now, the Espionage Act has never been interpreted this broadly, and for good reason. Using the Act to penalize the public receipt and distribution of government information leads to absurd conclusions.
Americans are paying too much for almost everything, because the United States has long treated its trucking industry as an artifact to be preserved rather than as an opportunity for innovation.
These ideas aim to advance the detailed policy solutions needed to foster public trust and implement fairness in the adoption of AI across diverse domains, from healthcare and government benefits to rural access, education, and worker protections.
The evidence is clear: algorithmic pay-setting is established in app-based work, and payroll/timekeeping failures show how software can produce systemic wage harm at scale
While a few states have taken steps to implement decision-making mechanisms for certain AI systems, too many leaders are simply accepting narratives about AI’s purported public benefit at face value – jumping to the “how” of AI implementation before thoroughly vetting potential systems and deciding whether they are appropriate to use at all.