A blistering critique of U.S. counterintelligence capabilities was authored by Michelle Van Cleave, the former National Counterintelligence Executive, in a case study prepared for the Project on National Security Reform. See Chapter 2 (pdf page 74) of this document (pdf).
“Fundamental Elements of the Counterintelligence Discipline” (pdf), published by the Office of the National Counterintelligence Executive and the ODNI in January 2006, is available here.
The CIA’s Office of General Counsel is profiled in a new paper (pdf) by former CIA assistant general counsel John Radsan, published in the Journal of National Security Law and Policy.
The missions and functions of the oddly named “U.S. Army Nuclear and Combating Weapons of Mass Destruction Agency” (formerly the Army Nuclear and Chemical Agency) are described in the new Army Regulation 10-16 (pdf), September 24, 2008.
“Exploring the U.S. Africa Command and a New Strategic Relationship with Africa” is the title of an August 2007 Senate Foreign Relations Committee hearing that has just been published.
The Congressional Research Service discussed “Africa Command: U.S. Strategic Interests and the Role of the U.S. Military in Africa” (pdf) in a report that was updated August 22, 2008.
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.