The process of declassifying national security records, which is hardly expeditious under the best of circumstances, will become slower as a result of the mandatory budget cuts known as sequestration.
Due to sequestration, “NARA has reduced funding dedicated to the declassification of Presidential records,” the National Archives and Records Administration (NARA) said in a report last week.
“Instead, NARA staff will prepare documents for declassification, in addition to their existing duties. This will slow declassification processes and delay other work, including FOIA responses and special access requests,” said the new report, which also identified several other adverse effects of the across-the-board cuts.
Meanwhile, because of the basic asymmetry between classification and declassification, there is no particular reason to expect a corresponding reduction in the rate at which new records are classified.
Classification is an integral part of the production of new national security information that cannot be deferred, while declassification is a distinct process that can easily be put on hold. Likewise, there is no dedicated budget for “classification” to cut in the way that NARA has cut declassification spending. And while Congress has erected barriers to declassification (such as the Kyl-Lott Amendment to prohibit automatic declassification of records without review), it has simultaneously allowed declassification requirements to go overlooked and unenforced.
Some declassification is actually mandated by law. A 1991 statute on the Foreign Relations of the United States series requires the Department of State to publish a “thorough, accurate, and reliable documentary record of major United States foreign policy decisions” no later than 30 years after the fact, necessitating the timely declassification of the underlying records. But law or no law, the government has not complied with this publication schedule.
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.
When properly structured — with specific numeric targets, secured financial obligations, independent monitoring, and meaningful enforcement — CBAs transform data center deals into durable community partnerships.