Integrating AI within MSA (Mythic System Analysis) Frameworks for the Algorithmic Translation and Preservation of Cultural Memory

Abstract

This study offers a combination of Artificial Intelligence (AI) into the frameworks of Mythic System Analysis (MSA), as a method of allowing culturally faithful algorithmic translation and maintenance of cultural memory. Meanings in cultural memory are transmitted in the form of myths, epics, folktales and ritual discourse, which rely on the fact that their meaning is based not only on surface semantics, but also on symbolic systems, motifs, archetypal roles, cosmological assumptions and performance registers. The translation method of conventional AI is usually maximized on fluency and lexical fidelity, which tend to flatten these structures, destroy taboo or sacred intonations, and may result in the creation of synthetic folklore. In order to fill this gap, the paper presents MSA-Guided Translation Pipeline (MGTP), a four stage structure: (A) myths have been decomposed into MSA elements by the community; (B) the community constraints and interest in variants, context, and provenance are represented as a living record in a cultural memory archive; (C) (B) community constraints and bias auditing has directed the multi-agent translation of the myth into machine readable in the form of a myth-graph; and (D) that the myth has been decomposed into MSA elements, and the The research also goes beyond the assessment measures of BLEU/ChrF mythic coherence, cultural nuance retention, preservation of plurality and risk of bias/appropriation, provides principles of governance based on consent, tiered access, human interpretive authority, transparent provenance, and the right to refusing digitization. The paper finishes off with a research agenda in the future of multimodal archiving, sovereign model implementation, cross-cultural ontology alignment, and longitudinal impact research.