发表机构
Santander AI Lab（圣安德烈人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究人工智能优化组织中何时不应自动化，提出PHP - AIO协议量化系统性风险并产生自动化决策，封闭形式度量$\rho(P)$形式化决策累积，应用于典型角色概况能产生不同结果，门控决策有一定鲁棒性。
AI 中文摘要
标准自动化投资回报率忽略了四类系统性风险——隐性知识侵蚀、恢复力降低、监管风险和社会制度资本退化——这些会影响组织的长期绩效。PHP - AIO（人工智能优化组织中的人类保护协议）是一种五步顺序决策协议，通过最终综合检查在角色层面量化这些未定价的系统性风险并产生可审计的自动化决策。一种封闭形式的自动化债务度量（$\rho(P)$）形式化了角色层面决策如何在多步骤过程中累积；只有监管机构强制的人工参与才能消除其警告。应用于代表性内部角色的典型概况时，可以为标准成本效益分析会统一自动化的候选对象产生不同结果。阈值敏感性分析证实，在四个代表性案例中的三个案例中，门控决策对至少14%的向上扰动具有鲁棒性。
英文摘要
Standard automation ROI misses four categories of systemic risk -- tacit knowledge erosion, resilience reduction, regulatory exposure, and socio-institutional capital degradation -- that affect long-term organizational performance. PHP-AIO (Protocol for Human Preservation in AI-Optimized Organizations) is a five-gate sequential decision protocol with a final composite check that quantifies these unpriced systemic risks at the role level and produces auditable automation decisions. A closed-form automation-debt measure ($ρ(P)$) formalises how role-level decisions accumulate across multi-step processes; its warning is neutralised only by a regulator-mandated human-in-the-loop anchor. Applied to stylised profiles of representative internal roles, PHP-AIO produces distinct outcomes -- automate, augment, hybrid, and preserve -- for candidates that standard cost-benefit analysis would uniformly automate. Threshold sensitivity analysis confirms the gate decisions are robust to upward perturbations of at least 14% in three of four representative cases. Keywords: AI governance, automation decision, human oversight, tacit knowledge, organizational resilience, financial services
