DoD’s Rotation to the Philippines, and More from CRS
“On March 18, 2016, the United States and the Republic of the Philippines announced the selection of five military sites that will host a rotation of U.S. military units. This marks the first time that U.S. units will be welcomed by the Republic on regularly scheduled visits since the last permanent garrisons were withdrawn in 1992,” according to a new brief from the Congressional Research Service. For background on the move, see DOD’s Rotation to the Philippines, CRS Insight, May 31, 2016.
Other new or newly updated CRS reports include the following.
A Shift in the International Security Environment: Potential Implications for Defense–Issues for Congress, updated May 31, 2016
Intellectual Property Rights Violations: Federal Civil Remedies and Criminal Penalties Related to Copyrights, Trademarks, Patents, and Trade Secrets, updated May 27, 2016
An Overview of Air Quality Issues in Natural Gas Systems, updated June 1, 2016
Changes in the Arctic: Background and Issues for Congress, updated May 31, 2016
Coast Guard Polar Icebreaker Modernization: Background and Issues for Congress, updated May 27, 2016
Constitutional Limits to Agency Independence, CRS Legal Sidebar, June 1, 2016
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.