Between September 2001 and February 2008, the Federal Bureau of Investigation initiated and closed the investigation of 85 reported leaks of classified intelligence information, “all of which concerned unauthorized disclosures of classified information to the media,” FBI Director Robert S. Mueller III told the Senate Intelligence Committee in a written response to questions (pdf) dated February 4, 2008.
“None of these cases reached prosecution,” he said. As of February 2008, “21 such cases are [still] under investigation.”
This information appeared in questions for the record that were appended to “Current and Projected National Security Threats to the United States” (pdf), a hearing before the Senate Intelligence Committee that was held January 11, 2007. The complete hearing volume was finally published last month, and the newly published questions for the record are excerpted here.
The Senate Intelligence Committee has renewed its practice of including questions for the record (QFRs) in published hearing volumes, for which one may be thankful, even when the answers are classified or are not provided by the agencies at all. Some additional QFRs, also newly published last month, appear in “Statutory Authorities of the Director of National Intelligence” (pdf), Senate Intelligence Committee, February 14, 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.