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.
The Insurability of Artificial Intelligence
English Summary
The report argues that the rapid integration of AI creates novel and complex risks—such as deepfakes, misinformation, and intellectual property disputes—that do not fit within existing insurance lines, challenging the technology's overall insurability. Evidence shows that while lawsuits center on IP, the scope of harm is expanding into privacy and product liability, and insurers are responding unevenly with a mix of exclusions and limited coverage. Policymakers must therefore focus on managing systemic, correlated AI risks, including shared model dependencies and universal attacks, to prevent market failure and guide necessary regulatory oversight.
中文摘要
本報告論述，AI的快速整合產生了新穎且複雜的風險——例如深度偽造、虛假資訊和智慧財產權糾紛——這些風險超出了現有保險的承保範圍，挑戰了該技術的整體可保性。證據顯示，儘管法律訴訟主要圍繞智慧財產權，但損害範圍正擴展至隱私權和產品責任，而保險公司的回應則不均衡，呈現出排除條款與有限承保的混合體。因此，政策制定者必須專注於管理系統性、相關聯的AI風險，包括共享模型依賴性和通用攻擊，以防止市場失敗，並指導必要的監管監督。
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