A recent DNI Open Source Center publication presents a guide to the Iraqi provincial elections that took place on January 31. The report was prepared prior to the elections and does not reflect their important results, but it does provide an informative overview of the electoral process, the Iraqi provincial council structure, and the thirty-six contending coalitions, with valuable individual profiles of the numerous coalition members.
Like most OSC analyses, it has not been approved for public release, but a copy was obtained by Secrecy News. See “Iraq: Provincial Elections Guide 2009” (pdf), Open Source Center Report, January 21, 2009. (For an initial assessment of the Iraqi election results by Philip Zelikow, see here.)
In a recent meeting with the Director of CIA Information Management Services, we reiterated our view that all unclassified, non-copyrighted publications of the Open Source Center (which is managed by CIA) should be made freely available to the public.
“I will convey the message,” the Director told us.
The Center for Democracy and Technology and Openthegovernment.org are inviting members of the public to suggest categories of government documents that they believe should be easily available online, but are not.
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.