Judicial Vacancies Rose Under Obama, & More from CRS
The number of district court vacancies during the Obama presidency grew from 41 vacancies in January 2009 to 75 vacancies in September 2016 — an unusual 83% increase, according to a new assessment from the Congressional Research Service.
By contrast, the number of vacancies decreased over the course of the George W. Bush Administration from 58 to 32 (a 45% decrease) and over the course of the Clinton Administration from 93 to 42 (a 55% decrease).
See U.S. District Court Vacancies: Overview and Comparative Analysis, CRS Insight, September 14, 2016
Other new and updated reports from the Congressional Research Service include the following.
U.S. Circuit Court Vacancies: Overview and Comparative Analysis, CRS Insight, September 14, 2016
How a National Infrastructure Bank Might Work, CRS Insight, September 15, 2016
International Food Aid Programs: Background and Issues, updated September 14, 2016
FDA Regulation of Medical Devices, updated September 14, 2016
Prospects in Colombia: Cease-Fire, Peace Accord Vote, and Potential Disrupters, CRS Insight, September 14, 2016
Nicaragua: In Brief, September 14, 2016
Navy Ship Names: Background for Congress, updated September 14, 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.