As Large Language Models (LLMs) increasingly serve as the primary interface for public engagement with cultural heritage, the infrastructure of memory is undergoing a profound transformation. Digital Humanities scholarship has frequently championed the generative potential of AI to foster new dialogues. However, an urgent need exists to audit the interpretative capacity of these systems to silence historical voices. This paper presents a novel computational methodology - High-Dimensional Vector Space Analysis - designed to quantify and visualize the "hermeneutical colonization" of non-Western and ancient concepts by commercially dominant models.

The study investigates how LLMs, trained predominantly on modern, Western, democratic corpora, structurally flatten the radical political vocabulary of the ancient world into compliant, modern categories. Using the Greek New Testament as a diagnostic case study for contested cultural heritage, we measured the "Semantic Displacement" of key political terms - specifically Basileia (Empire/Rule) and Ekklēsia (Assembly) - against modern semantic anchors.

Our findings reveal a statistically significant "Depoliticization Bias" within the vector geometry of state-of-the-art multilingual models (e.g., paraphrase-multilingual-mpnet-base-v2). Preliminary data indicates that the vector for Basileia aligns nearly twice as closely with the modern, spiritualized concept of "Kingdom" (0.59 similarity) than with its historical, political counterpart "Empire" (0.25 similarity). Similarly, Ekklēsia maps structurally to "Religious Institution" rather than "Democratic Assembly," effectively erasing the text’s original civic agency.

This phenomenon exceeds simple translation error; it constitutes a structural failure of digital infrastructure. If our "Infrastructures of Engagement" rely on these models, we risk automating a "monoculture of memory" where the past is rewritten to mirror the present status quo.

This paper proposes that true participatory engagement requires "Counter-Infrastructures": open-access, critical auditing tools that allow communities to visualize and contest the biases encoded in AI models. We outline the development of an open "Vector Atlas" - a tool enabling scholars and the public to "see" the distance between their cultural concepts and the model's representation of them. By making these hidden algorithmic displacements visible, we empower users to transform from passive consumers of AI content into critical auditors of AI memory.

This contribution aligns with the conference theme of "building resilient infrastructure" by demonstrating that resilience requires semantic sovereignty - the right of historical and cultural communities to define their own concepts against the flattening pressure of global algorithmic norms. We conclude by offering a framework for "Critical AI Hermeneutics" as a necessary literacy for digital citizenship in an age of automated interpretation.