From Protocols to Evidence: Bounded Claims for AI in Service of the Common Good
TL;DR
RISE AI provides an evidence architecture for making bounded claims about Responsibility, Inclusivity, Safety, and Empowerment, and a rupture test that evaluates deployment against explicit human and non-AI baselines.
Abstract
Claims that AI systems improve decisions, broaden access, reduce harm, or empower users can exceed what their evaluation supports. This gap motivates our ongoing work on RISE AI. In this paper, we examine the proposed framework in light of Pope Leo XIVs Magnifica Humanitas, particularly its treatment of human dignity, ownership, and technological power. We develop three linked propositions. First, AI adoption can reveal unmet human needs and institutional weaknesses, while deployment can change how institutions respond to them. We propose a rupture test to examine whether deployment repairs or compounds existing failures, substitutes for human support, or conceals problems behind improved performance. The test would compare deployment with existing practice and feasible human or non AI alternatives. Second, responsible AI must examine the institutional conditions surrounding a system, including who owns the infrastructure, controls its use, and bears responsibility for its consequences. Third, favorable evaluation cannot by itself establish respect for human dignity or moral and political legitimacy. We distinguish evidence-bounded deployment, which limits claims to what the evidence supports under specified conditions, from measurement-bounded governance, which identifies constraints that favorable results cannot override. We outline a proposed RISE AI framework for applying these propositions across Responsibility, Inclusivity, Safety, and Empowerment.
