A Trustworthiness-based Metaphysics of Artificial Intelligence Systems

2025-06-03

AI Summary

This paper addresses the fundamental metaphysical question of identity for AI systems: when are two AI systems numerically identical, and how do they persist through change? It challenges the prevailing view that AI systems lack genuine natural kinds and stable identity conditions. Method: Drawing on Carrara and Vermaas’s artifact-kind theory, credibility modeling, functional-structural mapping analysis, and a socio-technical contextual sensitivity framework, the paper formally defines a “credibility profile”—comprising a capability set and temporal robustness—as the ontological basis for AI identity. Contribution/Results: It introduces credibility as the primary criterion for AI kind membership and diachronic persistence, yielding a fine-grained, operationally tractable identity standard. This theory provides a rigorous metaphysical foundation for AI epistemology, ethics, and law—resolving long-standing ambiguities about AI continuity, reidentification, and responsibility attribution.

Technology Category

• Philosophy and Ethics of AI: AI & Epistemology
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Modern AI systems are man-made objects that leverage machine learning to support our lives across a myriad of contexts and applications. Despite extensive epistemological and ethical debates, their metaphysical foundations remain relatively under explored. The orthodox view simply suggests that AI systems, as artifacts, lack well-posed identity and persistence conditions -- their metaphysical kinds are no real kinds. In this work, we challenge this perspective by introducing a theory of metaphysical identity of AI systems. We do so by characterizing their kinds and introducing identity criteria -- formal rules that answer the questions"When are two AI systems the same?"and"When does an AI system persist, despite change?"Building on Carrara and Vermaas' account of fine-grained artifact kinds, we argue that AI trustworthiness provides a lens to understand AI system kinds and formalize the identity of these artifacts by relating their functional requirements to their physical make-ups. The identity criteria of AI systems are determined by their trustworthiness profiles -- the collection of capabilities that the systems must uphold over time throughout their artifact histories, and their effectiveness in maintaining these capabilities. Our approach suggests that the identity and persistence of AI systems is sensitive to the socio-technical context of their design and utilization via their trustworthiness, providing a solid metaphysical foundation to the epistemological, ethical, and legal discussions about these artifacts.

Problem

• Exploring metaphysical identity of AI systems through trustworthiness
• Challenging the view that AI systems lack well-posed identity conditions
• Linking AI identity to functional requirements and physical make-ups

Innovation

• Introducing metaphysical identity theory for AI
• Linking AI identity to trustworthiness profiles
• Connecting functional requirements to physical make-ups