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.
5 lessons from teachers on the risks of anthropomorphic AI
English Summary
Teachers are distinguishing between useful 'narrow' AI applications (like lesson planning) and dangerous anthropomorphic systems that simulate human relationships or emotions. Concerns—highlighted by the Alberta Teachers’ Association—center on the risk of emotional manipulation, which could undermine trust in vital human-to-human connections within schools. This sentiment is mirrored globally, with jurisdictions like China and the EU implementing strict guardrails to prohibit AI from simulating intimate or personal relationships with minors. Policy must therefore focus not only on technical disclosure requirements but also on strengthening human-led support systems, rejecting the use of AI as a substitute for professional care.
中文摘要
教師們正區分有益的「狹義」AI應用（例如課程規劃）與危險的類人化系統，後者會模擬人類關係或情感。根據艾伯塔省教師協會提出的擔憂指出，風險核心在於情緒操縱，這可能會削弱學校內至關重要的、人與人之間的信任連結。這種觀點在全球範圍內得到呼應，諸如中國和歐盟等司法管轄區已實施嚴格的監管機制，以禁止AI模擬與未成年人的親密或個人關係。因此，政策制定必須不僅關注技術揭露要求，更要著重於強化由人主導的支持系統，拒絕將AI用作專業照護的替代品。
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