On July 26, President Obama issued Presidential Policy Directive (PPD) 41 on United States Cyber Incident Coordination.
Aside from the intrinsic interest of this document, it signifies an unexplained burst in the production of Presidential Policy Directives since the public release of PPD 30 in June 2015. Instead of the previous average of around 5 presidential directives issued per year, President Obama produced about ten PPDs in the past 12 months.
With one exception, even the subject matter of PPDs 31 through 40 is publicly unknown.
The exception is PPD 35 on United States Nuclear Weapons Command and Control, Safety, and Security, which was issued on December 8, 2015. PPD 35 was publicly referenced by the Department of Defense in the April 2016 DoD Instruction 5210.42 on DoD Nuclear Weapons Personnel Reliability Assurance.
PPD 35 presumably modifies and supersedes President GW Bush’s 2003 National Security Presidential Directive (NSPD) 28, which was identically entitled United States Nuclear Weapons Command and Control, Safety, and Security.
But since neither the text of NSPD 28 nor that of PPD 35 have been made public, the substance of any changes that were made to U.S. nuclear weapons policy by the later directive is not known.
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.