Today’s national security classification system “relies on antiquated policies from another era that undercut its effectiveness today,” the Information Security Oversight Office told the President in a report released yesterday.
Modernizing the system is a “government-wide imperative,” the new ISOO annual report said.
But that is a familiar refrain by now. It is much the same message that was delivered with notable urgency by ISOO in last year’s annual report which found that the secrecy system is “hamstrung by old practices and outdated technology.”
The precise nature of the modernization that is needed is a subject of some disagreement. Is it a matter of improving efficiency in order to cope with expanding digital information flows? Or have the role of secrecy and the proper scope of classification changed in a fundamental way?
Whatever the goal, no identifiable progress has been made over the past year in overcoming those obsolete practices, and no new investment has been made in a technology strategy to help modernize national security information policy. In fact, ISOO’s own budget for secrecy oversight has been reduced.
Even agencies that are making use of advanced technologies such as artificial intelligence, machine learning, and predictive analytics in other areas have not considered their application to classification or declassification, ISOO said. “These technologies remain untapped in this area.”
At some point, the failure to update secrecy policy becomes a choice to let the secrecy system fail.
“We’re ringing the alarm bells as loud as we can,” said ISOO director Mark A. Bradley.
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.