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From Protocols to Evidence: Bounded Claims for AI in Service of the Common Good

arXiv 2609.11910 ↗First posted 10 Sep 2026cs.LGcited by 0

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Abstract (arXiv)

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.

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References · 5

  1. 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 384
  2. 2019Measurement and Fairnesscited by 308
  3. 2022Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineeringcited by 20
  4. 2021Meaningful human control: actionable properties for AI system developmentcited by 18
  5. 2025Justified Evidence Collection for Argument-based AI Fairness Assurancecited by 2

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