DoD Regulation on Formulating the Intelligence Budget
A recently revised Defense Department regulation (pdf) provides new detail on the preparation of the annual intelligence budget request, and on the documentation needed to support it.
The U.S. intelligence budget is comprised of two spending “aggregations”: the National Intelligence Program (NIP) and the Military Intelligence Program (MIP). (This configuration replaced the former National Foreign Intelligence Program, Joint Military Intelligence Program, and Tactical Intelligence and Related Activities.)
The NIP budget, which totaled $43.5 billion in 2007 according to last week’s official disclosure, funds intelligence to support national policy makers. The MIP budget, which probably amounts to at least another $10 billion, supports the Secretary of Defense, the military services, and military commanders in the field.
In practice, the distinction between the NIP and the MIP is not crystal clear, and several large “national” intelligence agencies — including NSA, DIA, NGA, NRO — also receive funding through the MIP.
A Defense Department Financial Management Regulation on “Intelligence Programs/Activities,” dated June 2007, presents the definitions of the intelligence budget aggregations, explains their classification levels, and describes the documentation that must be submitted to Congress to justify their appropriations.
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.