Updated below
Forty years after they were famously leaked by Daniel Ellsberg in 1971, the Pentagon Papers will be officially released next month at the Richard Nixon Presidential Library.
The National Archives announced this week that it “has identified, inventoried, and prepared for public access the Vietnam Task Force study, United States-Vietnam Relations 1945-1967, informally known as ‘the Pentagon Papers’.” As a result, 3.7 cubic feet of previously restricted textual materials will be made officially available at the Nixon Library on June 13, the Archives said in a May 10 Federal Register notice.
While any release of historical records is welcome, the official “disclosure” of the Pentagon Papers is in fact a sign of disarray in the government secrecy system. The fact that portions of the half-century old Papers remained classified until this year is a reminder that classification today is often completely untethered from genuine national security concerns.
On March 28, 2011 the National Declassification Center announced “the great news that the Office of the Secretary of Defense (OSD) has declassified the information of interest to them” in the Papers, clearing the way for next month’s public release.
Update: See Eleven Words in Pentagon Papers to Remain Classified.
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.