Treasury Classification Guide, and Other Resources
The Department of the Treasury has recently produced a consolidated classification guide, detailing exactly what kinds of Treasury information may be classified at what level and for how long. It is in such agency classification guides, not in high-level government-wide policy statements, that the nuts and bolts of government secrecy policy are to be found, and perhaps to be changed. See “Security Classification Guide” (pdf), Department of the Treasury, December 2010.
The Congressional Research Service yesterday offered its assessment of the Stuxnet worm, which was evidently designed to damage industrial control systems such as those used in Iran’s nuclear program. See “The Stuxnet Computer Worm: Harbinger of an Emerging Warfare Capability” (pdf), December 9, 2010.
Intelligence historian Jeffrey Richelson has written what must be the definitive account of the rise and fall of the National Applications Office, the aborted Department of Homeland Security entity that was supposed to harness intelligence capabilities for domestic security and law enforcement applications. The article, which is not freely available online, is entitled “The Office That Never Was: The Failed Creation of the National Applications Office.” It appears in the International Journal of Intelligence and Counter Intelligence, vol. 24, no. 1, pp. 65-118 (2011).
The latest issue of the Journal of National Security Law & Policy (vol. 4, no. 2) is now available online. Entitled “Liberty, terrorism and the laws of war,” it includes several noteworthy and informative papers on intelligence and security policy.
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.