论文提出AI责任评估新框架，结合机构基准与系统测试
From Protocols to Evidence: Bounded Claims for AI in Service of the Common Good
这篇论文从治理角度深入探讨了AI的责任问题，提出了一个结合机构基准和系统测试的新框架，对关注AI伦理和治理的读者很有价值。
这篇论文探讨了AI在服务公共利益时的责任问题，指出AI部署会干预现有机构失败的条件。它提出了一个‘破裂测试’来评估机构基准与系统评价，并区分了‘证据受限部署’和‘测量受限治理’。作者还基于教宗利奥十三世的思想，构建了RISE AI架构，用于做出关于责任、包容性、安全性和赋权性的有界、基于证据的声明。
From Protocols to Evidence: Bounded Claims for AI in Service of the Common Good
Artificial Intelligence does more than create a governance problem. It can also reveal where institutions have already failed to provide responsiveness, belonging, care, and accountability. Once deployed, AI becomes an intervention in those conditions. It can repair, compound, substitute for, or conceal the failures it encounters. Responsible AI must therefore evaluate both the system and the institutional rupture into which it is introduced. The move from principles to protocols is already underway. The EU AI Act, NIST AI RMF, ISO/IEC 42001, and assurance practices translate commitments into roles, requirements, records, oversight, and assessment. The harder questions are what these protocols actually establish, whose power they leave untouched, and where measurement must stop. Pope Leo XIV's Magnifica Humanitas provides a broader moral frame centered on dignity, technological power, and the common good. Drawing on that frame, we develop a rupture test that links institutional baselines to system evaluation. We distinguish evidence-bounded deployment, which limits claims to what has actually been evaluated, from measurement-bounded governance, which records constraints that favorable evidence cannot override. Within those limits, RISE AI provides an architecture for making bounded, evidence-based claims about Responsibility, Inclusivity, Safety, and Empowerment. Responsible AI requires better engineering, institutional repair, and continued moral and political judgment.
