The article argues that recent executive overreach and politicization have severely undermined established norms of U.S. civil-military relations. To restore professional standards, the incoming Congress must actively exercise its oversight powers, moving beyond mere criticism to demanding transparency in defense policy and personnel decisions. Key reforms include enforcing meritocratic promotion processes, requiring documented justification for senior officer removals, and preventing the partisan use of the military. These actions are crucial for bolstering public trust and ensuring the military remains a nonpartisan instrument of civilian control.
How are countries evaluating AI models?
English Summary
AI evaluation is rapidly evolving, moving beyond simple capability testing because General-Purpose AI (GPAI) models are opaque and lack defined boundaries. Policymakers must adopt a sociotechnical approach, assessing not just what a model can do in isolation, but how it performs when embedded in a product and used by humans. Effective governance requires multi-tiered testing—combining model testing with red teaming and field studies—to capture real-world risks and contextual factors. Consequently, regulatory strategy must shift from pre-deployment capability benchmarks to systemic, post-market monitoring that addresses the entire AI deployment chain.
中文摘要
人工智慧的評估方式正快速演進，已超越單純的能力測試。由於通用人工智慧（GPAI）模型具有不透明性且缺乏明確的邊界，政策制定者必須採取社會技術方法（sociotechnical approach）。這要求評估的重點不應僅限於模型孤立的能力，而應考察其嵌入產品並由人類使用時的實際表現。有效的治理需要多層次的測試——結合模型測試、紅隊演練（red teaming）和實地研究——以捕捉真實世界的風險和情境因素。因此，監管策略必須從事前部署的能力基準，轉向系統性的、上市後監測，以涵蓋整個人工智慧的部署鏈。
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