A new report from the Congressional Research Service describes the gene editing technology known as CRISPR-Cas9 and its dramatic implications for genetic engineering. The report also introduces the ethical, regulatory and policy questions that this technology is raising. See Advanced Gene Editing: CRISPR-Cas9, April 28, 2017.
Other new and updated reports from the Congressional Research Service include the following.
Law Enforcement Using and Disclosing Technology Vulnerabilities, April 26, 2017
Renegotiation of the North American Free Trade Agreement (NAFTA): What Actions Do Not Require Congressional Approval?, CRS Legal Sidebar, April 27, 2017
Softwood Lumber Dispute Lumbers On: Preliminary Countervailing Duties on Canadian Softwood Lumber Announced, CRS Legal Sidebar, April 28, 2017
Department of Defense Contractor and Troop Levels in Iraq and Afghanistan: 2007-2017, updated April 28, 2017
American War and Military Operations Casualties: Lists and Statistics, updated April 26, 2017
Armed Conflict in Syria: Overview and U.S. Response, updated April 26, 2017
U.S.-Mexico Economic Relations: Trends, Issues, and Implications, updated April 27, 2017
The Greek Debt Crisis: Overview and Implications for the United States, updated April 24, 2017
Iran’s Nuclear Program: Status, updated April 27, 2017
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.