Towards Computational Metaphilosophy: Reconstructing Philosophical Unity through Artificial Intelligence and Computational Methods.

Background: Modern philosophy is characterized by extensive fragmentation where a both analytic and continental approach survives side-by-side without any unifying framework, causing continuous controversy and lack of common methodologies. On the other hand, computational and AI methodologies (such as argument mapping, logic formalization systems, knowledge representation schemes, text mining, etc.) have been found to be beneficial in various areas of philosophy.

Problem: It needs to be noted that there has not been any previous attempt to connect the two trends together. Current research in metaphilosophy admits plurality but fails to provide a proper connection and computational philosophy uses specific methods on a case-by-case basis without considering the issue of unity.

Literature Gap: We find no prior framework openly using computational techniques to achieve philosophical unity. These methods have been used in limited ways, but there has never been an integration of these methods into a meta-methodology. There is no indication of the existence of a developed “computational metaphilosophy,” which combines epistemology, logic, information theory, and hermeneutics to address pluralism.

Aim: In this paper, we suggest the theory of Computational Metaphilosophy, which is based on the concept of using AI and computational techniques in order to restore philosophical unity. The definition is offered as follows: Computational Metaphilosophy is the use of formally and computationally oriented approaches to philosophy that include such elements as argument maps, logic, ontologies, knowledge graphs, etc.

Methodology: In our framework, every method is considered as an instrument for philosophical thinking. The argument mapping technique will help us reveal the logical structure of disputes, formal logic will help us to find any inconsistencies in arguments, knowledge graphs and ontologies will allow us to classify concepts and create links between them, conceptual engineering (with the help of LLMs) will enable us to clarify definitions, text mining will help us find trends and correlations in the corpora and, finally, semantic network analysis will help us find out the conceptual relatedness. Importantly, all computational results will be analyzed philosophically (computational hermeneutics).

Contribution: The main contribution is conceptual: establishing Computational Metaphilosophy as a new research paradigm. We will delineate its foundations in metaphilosophy, philosophy of science, logic, hermeneutics, epistemology and philosophy of information, explaining how each justifies and constrains computational methods. We clarify how CM differs from both “philosophy of AI” and from traditional metaphilosophy, filling a true gap in the literature.

Significance: By clearly addressing philosophical unity through computational enrichment, this study responds directly to the conference theme. It shows how unity can emerge within diversity of method: computational tools turn out to be a medium for interdisciplinary dialogue. For example, joining analytic logical structures with continental semantic networks could reveal shared assumptions; mining large text collection could identify convergent themes among apparently disimilar authors. CM thus shows multidisciplinary inquiry, using AI to strengthen(not replace) philosophical insight and pointing toward a future in which philosophy itself evolves in the digital age.