Secrecy News Purged from State Dept History Mailing List
Secrecy News was removed from the distribution list for the U.S. State Department history publication “Foreign Relations of the United States” (FRUS) after we reported on errors in several FRUS volumes on March 24 and 26, 2008.
A spokesman for the State Department Historian’s Office confirmed that officials had ordered the removal of Secrecy News from the FRUS mailing list in response to our critical coverage.
In an email message to the series editor yesterday, I asked the Historian’s Office (HO) to reconsider its action. To do so would serve the best interests of FRUS, I suggested.
“I know that a sizable fraction of my Secrecy News mailing list (which now exceeds 13,500 self-selected subscribers) has an interest in FRUS publications. Many of those subscribers are unlikely to be part of other existing networks of academics and historians through which news of FRUS is disseminated,” I wrote.
“I would also willingly publish any criticism of my own writing that HO personnel or HAC [Historical Advisory Committee] members felt was warranted,” I added.
The request to reinstate Secrecy News on the FRUS mailing list awaits a decision by the State Department Historian, Dr. Marc J. Susser.
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.