arXiv AI

The Authority Resolution Framework: A Five-Domain Ontology for Governing Who and What Decides, at Scale

arXiv:2608. 15832v1 Announce Type: new Abstract: As AI systems become increasingly capable of autonomous action, determining whether an agent is technically capable of performing an action is insufficient: the system must also determine whether the action is authorised in its context.

arXiv AI
Aug 28

Knowledge Cards: Structured Knowledge for AI Systems

The paper introduces Knowledge Cards, a new structured artefact designed to capture validated knowledge about specific concepts that AI systems use to make decisions. Unlike existing model, data, and system cards, Knowledge Cards focus on the layer between inputs and outputs, documenting entities, relationships, reasoning patterns, conditions for validity, and provenance, all grounded in a formal domain ontology and signed off by a domain expert. Prototype cards have been created in the energy and pharmaceutical domains, and the schema is released as a public draft for community engagement.

By Liliana Ferreira
arXiv AI
Jun 19

Deontic Policies for Runtime Governance of Agentic AI Systems

arXiv:2606. 19464v1 Announce Type: new Abstract: Autonomous agentic AI systems driven by Large Language Models (LLMs) introduce a new class of security, privacy, and compliance challenges: an agent that can invoke tools, manipulate data, install software, and coordinate with peer agents across organizational boundaries must be constrained not just by authentication and access control, but by the full structure of enterprise governance.

By Anupam Joshi, Tim Finin, Karuna Pande Joshi, Lalana Kagal
arXiv Machine Learning
Sep 11

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

The paper argues that AI should be evaluated not only by principles but by concrete protocols that translate commitments into roles, requirements, records, oversight, and assessment. It introduces a rupture test linking institutional baselines to system evaluation, and distinguishes evidence‑bounded deployment from measurement‑bounded governance. The authors propose the RISE AI architecture to make bounded, evidence‑based claims about Responsibility, Inclusivity, Safety, and Empowerment, emphasizing the need for engineering, institutional repair, and ongoing moral judgment.

By Nitesh V. Chawla, Paulo Benanti
arXiv AI
Jul 14

A Theory of Least Autonomy in AI

arXiv:2607. 09744v1 Announce Type: new Abstract: Least privilege, the principle that an identity should hold only the permissions strictly required for its task, has been a foundational primitive of access control for decades.

By Christophe Parisel
arXiv AI
Oct 28

Constraining the Commit Boundary: A DIO/ODI-Informed Authorization Guardrail for Agentic Entity Resolution

The paper introduces a Digital Identity Optimization (DIO) and Ontology of Digital Identity (ODI)-informed authorization guardrail for Agentic Entity Resolution (ER) systems. It defines a commit boundary that restricts evidence used for persistent identity changes, employing a two‑phase supervisor to ensure claim‑specific authorization, conflict resolution, scope adherence, and version validity. The work outlines a conceptual architecture and an evaluation protocol but does not provide implementation or empirical validation.

By Daniel Ber\'anek