The 2019 budget request for U.S. Special Operations Command — $13.6 billion — is 10% higher than the 2018 level and is the largest budget request ever submitted by US SOCOM.
U.S. special operations forces, which are currently deployed in 90 countries, have more than doubled in size from 33,000 personnel in 2001 to around 70,000 personnel in early 2018. Next year’s budget, if approved, would make them larger still.
For a newly updated overview from the Congressional Research Service, see U.S. Special Operations Forces (SOF): Background and Issues for Congress, April 13, 2018.
Other recent CRS reports that have not otherwise been made publicly available include the following.
Federal Election Commission: Membership and Policymaking Quorum, In Brief, April 12, 2018
Regulatory Reform 10 Years After the Financial Crisis: Systemic Risk Regulation of Non-Bank Financial Institutions, April 12, 2018
Abortion At or Over 20 Weeks’ Gestation: Frequently Asked Questions, April 11, 2018
Millennium Challenge Corporation, updated April 12, 2018
Latin America and the Caribbean: Fact Sheet on Leaders and Elections, updated April 11, 2018
Softwood Lumber Imports From Canada: Current Issues, updated April 12, 2018
Yemen: Civil War and Regional Intervention, updated April 12, 2018
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.