社会对 AI 人类劳动自动化的适应
Societal Adaptation to AI Human-Labor Automation
AI总结：
论文基于社会适应框架构建 AI 人类劳动自动化威胁模型，系统比较能力改变型与适应型干预，强调长期教育、收入与心理替代及前瞻治理。
摘要：
AI 正以前所未有的速度改变人类劳动——训练效果每年提升 10 倍。本文运用 Bernardi 等人的社会适应框架，分析社会如何适应 AI 驱动的人类劳动自动化（HLA）。论文借鉴一般自动化经济学文献和近期 AI 发展，构建了一个“威胁模型”。该威胁模型以大规模失业及其社会经济后果为核心，并假设存在一种处于 AGI 全面接管与快速创造就业之间的非二元情景。分析同时探讨了塑造 AI 发展方式的“能力改变型干预”（CMIs），以及帮助社会进行调整的“适应型干预”（ADIs）。所分析的关键干预包括：引导 AI 发展朝向人类互补能力、实施 human-in-the-loop（人在回路中）要求、对自动化征税、全面调整教育方向，以及在物质和社会层面对工作进行替代。虽然 CMIs 可以在短期内减缓转型，但显著自动化仍不可避免。长期适应需要 ADIs——从教育改革，到为工作的收入收益和心理收益提供替代。成功取决于通过“if-then commitments（如果—那么承诺）”等机制进行预先准备，以及制定灵活、准确且避免设定错误的监管。本文对 HLA 干预及其潜在影响和挑战进行结构化分析，旨在为 AI 经济的整体性 AI 治理战略提供指导。
英文摘要：
AI is transforming human labor at an unprecedented pace - improving 10$\times$ per year in training effectiveness. This paper analyzes how society can adapt to AI-driven human-labor automation (HLA), using Bernardi et al.'s societal adaptation framework. Drawing on literature from general automation economics and recent AI developments, the paper develops a "threat model." The threat model is centered on mass unemployment and its socioeconomic consequences, and assumes a non-binary scenario between full AGI takeover and swift job creation. The analysis explores both "capability-modifying interventions" (CMIs) that shape how AI develops, and "adaptation interventions" (ADIs) that help society adjust. Key interventions analyzed include steering AI development toward human-complementing capabilities, implementing human-in-the-loop requirements, taxation of automation, comprehensive reorientation of education, and both material and social substitutes for work. While CMIs can slow the transition in the short-term, significant automation is inevitable. Long-term adaptation requires ADIs - from education reform to providing substitutes for both the income and psychological benefits of work. Success depends on upfront preparation through mechanisms like "if-then commitments", and crafting flexible and accurate regulation that avoids misspecification. This structured analysis of HLA interventions and their potential effects and challenges aims to guide holistic AI governance strategies for the AI economy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
