Too Many Secrets, the Greatest Math Discovery, and More
The Wikileaks publication of tens of thousands of classified U.S. military records last week is inevitably prompting a review of information security practices to identify remedial steps. I have been arguing that one of those steps ought to be a rethinking of classification policy. “The reform that may be needed more urgently than any other is a careful reduction in the size of the secrecy system.” See “Afghan Leaks: Is the U.S. Keeping Too Many Secrets?” by Alex Altman, Time, July 30.
The Department of Defense has updated its doctrine on “foreign internal defense,” which refers to actions taken to support a foreign government’s efforts to combat domestic subversion, insurgency or terrorism. See Joint Publication 3-22, “Foreign Internal Defense,” July 12, 2010.
“The Army in Multinational Operations” is the subject of a newly updated U.S. Army Field Manual, FM 3-16, May 2010.
Michel de Montaigne (1533-1592), whose essays transformed Western consciousness and literature, was not capable of solving basic arithmetic problems. And most other people would not be able to do so either, if not for the invention of decimal notation by an unknown mathematician in India 1500 years ago. That is the contention of a neat little essay recently published by the Department of Energy (based in part on a book by Georges Ifrah). See “The Greatest Mathematical Discovery?” by David H. Bailey and Jonathan M. Borwein, May 12, 2010.
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.