Upon publication this month, “Legacy of Ashes” by Tim Weiner of the New York Times has all at once become the best single source on the history of the Central Intelligence Agency.
The book synthesizes entire shelves of prior studies, and surpasses them with the fruits of deep archival research and two decades of on-the-record interviews. The detailed endnotes provide pointers for further investigation.
Somewhat oddly, the book is framed as a “warning.”
“It describes how the most powerful country in the history of Western civilization has failed to create a first-rate spy service. That failure constitutes a danger to the national security of the United States,” Mr. Weiner writes.
The implication here is that the standard for excellence has been set by another intelligence agency, one that unlike CIA is “first rate.” If so, it would be interesting to know which agency that is. (Not the KGB, certainly, nor the SIS or Mossad.)
If not, and if there is no consistently “first rate” intelligence service, then the problem may lie in an exaggerated expectation that any secret intelligence service can reliably “see things as they are in the world.”
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.