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.
AI, 7-OH, and the Illusion of Scientific Uncertainty
English Summary
This article argues that large language models frequently project unwarranted certainty about contested scientific questions, particularly in public health debates. Using 7-hydroxymitragynine (7-OH), an alkaloid in kratom, as a case study, the author demonstrates how AI systems confidently describe it as a highly potent opioid with serious overdose risks, while recent scientific literature reveals ongoing debate about its pharmacology and safety profile—with evidence suggesting more complex, atypical receptor signaling and rare fatal overdoses. LLMs are trained to provide confident syntheses rather than distinguish between settled science and ongoing controversies, risking the amplification of moral panics around emerging substances. The author argues against regulatory approaches to resolve scientific disagreement and instead advocates for transparency about uncertainty, continued open debate, and scrutiny of confident claims from all sources. Policymakers considering substance restrictions should recognize that today's scientific controversy may become tomorrow's accepted therapy.
中文摘要
本文論述大型語言模型（LLMs）在處理爭議性科學問題，特別是在公共衛生辯論中，經常會呈現出不必要的確信。作者以克拉托姆（kratom）中的鹼化合物7-羥基米特拉庚質（7-OH）為案例，展示了人工智慧系統如何自信地將其描述為具有嚴重過量風險的高度效力鴉片類藥物。然而，最新的科學文獻卻揭示了其藥理學和安全性方面仍在持續爭論——證據顯示其可能存在更為複雜、非典型的受體訊號傳導，且致命過量案例極為罕見。LLMs的訓練目的在於提供自信的綜合性描述，而非區分已定論的科學事實與正在進行的爭議，這有時會導致圍繞新興物質產生道德恐慌的放大化。作者反對以監管方式來解決科學分歧，反而主張應提高關於不確定性的透明度、維持持續的公開辯論，並對所有來源的自信聲明進行嚴格審查。對於考慮實施物質限制的政策制定者而言，必須認識到，今日的科學爭議，可能就是明日被接受的療法。
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