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.
Legal and Policy Approaches to Mitigate Catastrophic Harms from AI
English Summary
A Delphi expert elicitation of 16 AI and policy experts evaluated 11 legal and policy approaches to reduce catastrophic AI harms, finding that mandatory measures face significant political and practical infeasibility, while incentives to find and disclose risks and voluntary safety standards emerged as most promising. Experts rated nearly all categories as desirable but questioned feasibility in the current U.S. political environment, with effectiveness varying substantially by actor type—highest for AI developers (3.3 average), lower for nonmalicious users (3.0), and lowest for malicious users (2.3). The most viable approaches require no federal government involvement and can be implemented through industry commitments and state-level action, including structured bug bounty programs, legal safe harbors for researchers, and coordinated vulnerability disclosure processes. Rather than waiting for comprehensive federal legislation, policymakers should pursue incremental, near-term measures that foster transparency through developer incentives and establish voluntary standards as scaffolding for future mandatory requirements. The analysis reflects growing skepticism about traditional regulatory approaches in the AI domain, with experts increasingly viewing private-sector and state-level action as more feasible pathways for near-term risk mitigation.
中文摘要
一項包括16位AI和政策專家的德爾菲專家諮詢研究評估了11種法律和政策方法以減少災難性AI危害。研究發現強制性措施面臨重大的政治和實踐可行性障礙，而尋找和披露風險的激勵措施與自願安全標準成為最具前景的方案。專家認為幾乎所有類別都值得採納，但對其在當前美國政治環境中的可行性提出質疑。有效性因參與者類型而有實質性差異——對AI開發商最高（平均3.3），對非惡意使用者較低（3.0），對惡意使用者最低（2.3）。最可行的方法無需聯邦政府參與，可通過業界承諾和州級行動實現，包括結構化的漏洞賞金計劃、面向研究人員的法律安全港灣，以及協調的漏洞披露流程。與其等待全面的聯邦立法，政策制定者應追求增量式的近期措施，通過開發商激勵促進透明度，並建立自願標準作為未來強制性要求的基礎。該分析反映出對AI領域傳統監管方法日益增長的懷疑，專家們越來越傾向於認為私營部門和州級行動是更可行的近期風險緩解途徑。
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