# A Trustworthiness-based Metaphysics of Artificial Intelligence Systems

arXiv: https://arxiv.org/abs/2506.03233 · HTML: https://mlsanity.com/paper/2506.03233/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 59 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [Twenty-Four Years of Empirical Research on Trust in AI: A Bibliometric Review of Trends, Overlooked Issues, and Future Directions](https://mlsanity.com/paper/2309.09828/) (2023)
- [Diachronic and synchronic variation in the performance of adaptive machine learning systems: The ethical challenges](https://mlsanity.com/paper/2504.08861/) (2025)

## Built on by (most cited first, 6 of 6)

- [Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles](https://mlsanity.com/paper/2510.21293/) (2025)
- [High-Risk AI Systems and the Problem of Identity in the European AI Act](https://mlsanity.com/paper/2605.23922/) (2026)
- [A Category Theory Account of AI Identity](https://mlsanity.com/paper/2607.00220/) (2026)
- [Update Opacity: Epistemic Accessibility and Governance Under AI System Change](https://mlsanity.com/paper/2606.00037/) (2026)
- [A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governance](https://mlsanity.com/paper/2607.16130/) (2026)
- [Operational Identity: A Finite Audit of Declared and Implemented Rules of Sameness](https://mlsanity.com/paper/2607.20729/) (2026)

## Abstract (arXiv)

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.