The article argues that the Federal Reserve's recent rate hike decision, while justifiable on inflation grounds, highlights a critical lack of an underlying, transparent policy framework. The core problem is that the Fed's decisions appear discretionary, leading to market uncertainty because the committee's judgment, rather than clear data, dictates policy shifts. The author proposes that the Fed adopt a formal monetary policy rule—an algebraic formula linking the target rate to indicators like inflation and unemployment—to replace subjective guidance. Implementing such a rule would provide market predictability, enhance transparency, and shield the Fed from political attacks by making deviations from the standard easily quantifiable.
Assessing the state of AI adoption across the federal government
English Summary
AI adoption is a bipartisan priority within the federal government, showing significant growth in reported use cases across various agencies. However, the report finds that adoption remains uneven, heavily concentrated in large agencies, and is significantly constrained by structural bottlenecks. Key challenges include workforce capacity limitations, a risk-averse culture, and systemic issues in procurement and funding. To accelerate responsible deployment, policy must focus on expanding technical talent and AI literacy, streamlining outdated acquisition processes, and enhancing transparency to build public trust in high-impact AI systems.
中文摘要
人工智慧（AI）的採用已成為聯邦政府跨黨派的優先議題，各機構報告的使用案例顯著增長。然而，本報告指出，其採用仍不均衡，高度集中於大型機構，且受到結構性瓶頸的嚴重制約。主要的挑戰包括勞動力能力限制、風險規避的文化，以及採購和資金方面的系統性問題。為加速負責任的部署，政策制定必須著重於擴展技術人才和AI素養、簡化過時的採購流程，並提高透明度，以建立公眾對高影響力AI系統的信任。
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