Numerous new reports of the Congressional Research Service on subjects of public interest and concern have been issued lately. Yet by design, they are not made readily available to the public. They include the following.
“The Department of Defense Rules for Military Commissions: Analysis of Procedural Rules and Comparison with Proposed Legislation and the Uniform Code of Military Justice” (pdf), updated July 25, 2006.
“Hamdan v. Rumsfeld: Military Commissions in the ‘Global War on Terrorism'” (pdf), July 6, 2006.
“Military Tribunals: Historical Patterns and Lessons” (pdf), July 9, 2004.
“Iran: U.S. Concerns and Policy Responses” (pdf), updated July 31, 2006.
“Israeli-Arab Negotiations: Background, Conflicts, and U.S. Policy” (pdf), updated July 25, 2006.
“Lebanon” (pdf), updated July 24, 2006.
“European Approaches to Homeland Security and Counterterrorism” (pdf), July 24, 2006.
“China and Proliferation of Weapons of Mass Destruction and Missiles: Policy Issues” (pdf), updated July 17, 2006.
“Banning Fissile Material Production for Nuclear Weapons: Prospects for a Treaty (FMCT)” (pdf), July 14, 2006.
“North Korean Ballistic Missile Threat to the United States” (pdf), updated July 6, 2006.
“International Small Arms and Light Weapons Transfers: U.S. Policy” (pdf), updated June 27, 2006.
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.