Since 1887, administrative law has navigated a "capability-accountability trap": technological change forces government to become more sophisticated, but sophistication renders agencies opaque to generalist overseers like the courts and Congress. The law's response--substituting procedural review for substantive oversight--has produced a sedimentary accretion of requirements that ossify capacity without ensuring democratic control. This Article argues that the Supreme Court's post-Loper Bright retrenchment is best understood as an effort to shrink administration back to comprehensible size in response to this complexification. But reducing complexity in this way sacrifices capability precisely when climate change, pandemics, and AI risks demand more sophisticated governance. AI offers a different path. Unlike many prior administrative technologies that increased opacity alongside capacity, AI can help build "scrutability" in government, translating technical complexity into accessible terms, surfacing the assumptions that matter for oversight, and enabling substantive verification of agency reasoning. This Article proposes three doctrinal innovations within administrative law to realize this potential: a Model and System Dossier (documenting model purpose, evaluation, monitoring, and versioning) extending the administrative record to AI decision-making; a material-model-change trigger specifying when AI updates require new process; and a "deference to audit" standard that rewards agencies for auditable evaluation of their AI tools. The result is a framework for what this Article calls the "Fourth Settlement," administrative law that escapes the capability-accountability trap by preserving capability while restoring comprehensible oversight of administration.

翻译：自1887年以来，行政法始终面临着一项"能力-问责陷阱"：技术变革迫使政府变得日益精密，但精密化却使机构对法院和国会等非专业监督者而言变得难以理解。法律对此的回应——以程序审查替代实质性监督——已形成层层累积的要求，在固化行政能力的同时却未能确保民主控制。本文认为，最高法院在洛珀·布莱特案后的缩减趋势，最好被理解为应对此种复杂化而将行政规模收缩至可理解范围的努力。然而，正当气候变化、流行病及人工智能风险要求更精良的治理时，这种降低复杂性的方式反而牺牲了行政能力。人工智能提供了不同路径。与许多先期行政技术在提升能力的同时增加不透明性不同，人工智能有助于构建政府的"可审查性"：将技术复杂性转化为可理解术语，揭示对监督至关重要的预设条件，并实现对机构推理过程的实质性验证。本文提出三项行政法内的理论创新以实现这一潜力：模型与系统档案（记录模型目的、评估、监控与版本控制），将行政记录延伸至人工智能决策领域；实质性模型变更触发机制，规定人工智能更新何时需要启动新程序；以及"审计遵从"标准，奖励机构对其人工智能工具进行可审计评估。最终形成本文所称"第四次调和"的框架——一种通过保留行政能力同时恢复对行政的可理解监督，从而摆脱能力-问责陷阱的行政法体系。