Noteworthy new publications from the Congressional Research Service include the following.
Defense Primer: Electronic Warfare, CRS In Focus, updated April 12, 2019
U.S. Military Electronic Warfare Research and Development: Recent Funding Projections, CRS Insight, April 15, 2019
Assessing Commercial Disclosure Requirements under the First Amendment, April 23, 2019
The National Institutes of Health (NIH): Background and Congressional Issues, updated April 19, 2019
The Federal Communications Commission: Current Structure and Its Role in the Changing Telecommunications Landscape, April 18, 2019
Selected Homeland Security Issues in the 116th Congress, April 23, 2019
Can the President Close the Border? Relevant Laws and Considerations, CRS Legal Sidebar, April 12, 2019
Central American Migration: Root Causes and U.S. Policy, CRS In Focus, March 27, 2019
Cooperative Security in the Middle East: History and Prospects, CRS In Focus, updated April 11, 2019
International Criminal Court: U.S. Response to Examination of Atrocity Crimes in Afghanistan, CRS Insight, updated April 16, 2019
Nuclear Cooperation: Part 810 Authorizations, CRS In Focus, April 18, 2019
U.S. War Costs, Casualties, and Personnel Levels Since 9/11, CRS In Focus, April 18, 2019
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.