New Research Proposes Hermeneutic Framework for Evaluating Generative AI as Cultural Technology

A new research paper argues that generative AI systems should be understood as cultural technologies rather than tools with culture as a measurable variable. The paper, published on arXiv under identifier 2604.16403v1, introduces computational hermeneutics as an emerging framework for evaluating these systems. Drawing from hermeneutic theory in the humanities, the authors contend that generative AI functions as "context machines" that must address three core interpretive challenges: situatedness, where meaning emerges only within specific contexts; plurality, acknowledging multiple valid interpretations can coexist; and ambiguity, recognizing interpretations naturally conflict. The research proposes three principles for hermeneutic evaluation: benchmarks should be iterative rather than one-off assessments, include human participants alongside machines, and measure cultural context in addition to model outputs. This perspective represents a nascent paradigm for both designing and evaluating generative AI systems, shifting focus from technical performance to interpretive capabilities. The framework suggests current evaluation methods often fail to account for the fundamental role culture plays in how these systems operate.