New and updated publications from the Congressional Research Service that Congress has withheld from online public access include the following.
Staff Pay Levels for Selected Positions in House Member Offices, 2009-2013, November 3, 2014
Staff Pay Levels for Selected Positions in Senators’ Offices, FY2009-FY2013, November 3, 2014
Congressional Action on FY2015 Appropriations Measures, November 5, 2014
The G-20 Summit: Brisbane, November 15-16, 2014, CRS Insights, November 5, 2014
Treating Ebola Patients in the United States: Health Care Delivery Implications, CRS Insights, November 4, 2014
EPA’s Clean Power Plan Proposal: Are the Emission Rate Targets Front-Loaded?, CRS Insights, November 3, 2014
How Will the Federal Reserve “Normalize” Monetary Policy After QE?, CRS Insights, October 30, 2014
Federal Taxation of Marijuana Sellers, CRS Legal Sidebar, November 6, 2014
Voter Identification Requirements: Background and Legal Issues, November 3, 2014
Qatar: Background and U.S. Relations, November 4, 2014
Immigration Legislation and Issues in the 113th Congress, November 4, 2014
Border Security: Immigration Inspections at Ports of Entry, October 31, 2014
Renewable Energy R&D Funding History: A Comparison with Funding for Nuclear Energy, Fossil Energy, and Energy Efficiency R&D, October 10, 2014
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.