Scientific Basis of EPA Actions, and More from CRS
Noteworthy new products from the Congressional Research Service that Congress has withheld from online public distribution include the following.
U.S. Trade Concepts, Performance, and Policy: Frequently Asked Questions, November 17, 2014
Supreme Court Hears Oral Argument in Federal Whistleblower Case, CRS Legal Sidebar, November 14, 2014
Scientific Basis of Environmental Protection Agency (EPA) Actions: H.R. 1422 and H.R. 4012, CRS Insights, November 17, 2014
International Climate Change Financing: The Green Climate Fund (GCF), November 17, 2014
The Battle over Cable Boxes, CRS Insights, November 14, 2014
The Regional Greenhouse Gas Initiative: Lessons Learned and Issues for Policy Makers, November 14, 2014
Keystone XL Pipeline: Overview and Recent Developments, November 13, 2014
Federal Proposals to Tax Marijuana: An Economic Analysis, November 13, 2014
Childhood Overweight and Obesity: Data Brief, November 13, 2014
Veterans and Homelessness, November 13, 2014
When Will DOD Modernize its Electronic Health Records Systems?, CRS Insights, November 13, 2014:
President Obama’s November 2014 Visit to China: The Bilateral Agreements, CRS Insights, November 13, 2014
Defense: FY2015 Authorization and Appropriations, November 13, 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.