制度窗口：针对不同职业和司法辖区的责任信号校准以保留人类 fallback 能力
The Institutional Window: Occupation- and Jurisdiction-Specific Calibration of Liability Signaling for Preserved Human Fallback Capability
- Aegis Compliance and Strategies OÜ（Aegis合规与战略有限责任公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文聚焦生成式AI背景下责任信号失效问题，引入制度楔差构建信号窗口，校准5种职业和7个司法辖区，发现责任制度影响人类 fallback 能力，企业需针对司法辖区约束边际调整策略。
AI中文摘要：
问题定义：当生成式AI产出的专业成果让客户无法区分其与合格提供者的成果时，经典的基于成本的质量信号会失效，仅结果依赖型承诺能区分不同类型。此类承诺可证明企业构建的内生易逝资产：人类 fallback 能力，该能力通过让员工参与AI处理的案件得以维持，否则会衰退。现有研究未明确该机制的适用范围，本文探究其在不同职业和责任制度中仍具信息性的场景。方法与结果：我们引入责任上限与保留风险之间的制度楔差，该楔差由四个法律基本要素产生：成本规则、惩罚条款的可执行性、私人责任被国家责任或集体赔偿取代、合同责任的强制上限。该楔差将分离类型空间压缩至信号窗口内，窗口上限由偿付能力和惩罚原则界定，下限由标准条款控制使上限低于某一阈值的情况界定。我们基于公开证据对5种职业和7个司法辖区进行校准：在普通法约定损害赔偿渠道中，当可验证性低于1/m时，可验证性增量不可用，将合同问题转化为运营问题；可验证性投资在窗口受上限约束时拓宽窗口，受下限约束时收窄窗口；在基于智能体的市场中，在测试的政策类别内，所有窗口为空的单元均收敛至零参与度和技能崩溃。管理启示：责任制度是劳动力能力工具，而非仅风险分配工具；企业应针对各司法辖区的约束边际，上限和集体赔偿均会抑制维持 fallback 能力的信号。
英文摘要：
Generative AI makes expert output uninformative about the human fallback capability its provider keeps in reserve. A liability commitment can signal that capability: a provider whose staff rescue more of the cases the AI fails pays damages less often, so the commitment certifies an asset built by keeping people on cases and lost by taking them off. This paper asks where contract law leaves room for such a signal. Four legal primitives map a posted cap into retained exposure and close the message space from both sides: litigation viability, the penalty doctrine, displacement of liability to the state or an indemnity pool, and mandatory control of standard terms. Where the law leaves an option of zero exposure, low types pool at zero, intermediate types separate on a schedule anchored at the mandatory floor, and high types pool at the ceiling; where it does not, separation starts at the bottom type. A statutory floor removes the lower pool but pushes the schedule toward the ceiling, so its net effect is not monotone. The penalty doctrine decides whether recovery adequate for deterrence can be restored by contract or only by investing in verifiability. Holding fallback skill constant demands more human engagement the less often the AI fails. A calibration to five occupations and twelve legal configurations in Germany, Austria, Switzerland, the United Kingdom and the United States locates the binding margin of each cell and supports no ranking by legal family or contracting channel.
