Computational Hermeneutics: Evaluating generative AI as a cultural technology

Computer Science > Artificial Intelligence [Submitted on 31 Mar 2026] Computational Hermeneutics: Evaluating generative AI as a cultural technology Cody Kommers, Ruth Ahnert, Maria Antoniak, Emmanouil Benetos, Steve Benford, Mercedes Bunz, Baptiste Caramiaux, Shauna Concannon, Martin Disley, James Dobson, Yali Du, Edgar Duéñez-Guzmán, Kerry Francksen, Evelyn Gius, Jonathan W. Y. Gray, Ryan Heuser, Sarah Immel, Richard Jean So, Sang Leigh, Dalaki Livingston, Hoyt Long, Meredith Martin, Georgia Meyer, Daniela Mihai, Ashley Noel-Hirst, Kirsten Ostherr, Deven Parker, Yipeng Qin, Jessica Ratcliff, Emily Robinson, Karina Rodriguez, Adam Sobey, Ted Underwood, Aditya Vashistha, Matthew Wilkens, Youyou Wu, Yuan Zheng, Drew Hemment Generative AI systems are increasingly recognized as cultural technologies, yet current evaluation frameworks often treat culture as a variable to be measured rather than fundamental to the system's operation. Drawing on hermeneutic theory from the humanities, we argue that GenAI systems function as "context machines" that must inherently address three interpretive challenges: situatedness (meaning only emerges in context), plurality (multiple valid interpretations coexist), and ambiguity (interpretations naturally conflict). We present computational hermeneutics as an emerging framework offering an interpretive account of what GenAI systems do, and how they might do it better. We offer three principles for hermeneutic evaluation -- that benchmarks should be iterative, not one-off; include people, not just machines; and measure cultural context, not just model output. This perspective offers a nascent paradigm for designing and evaluating contemporary AI systems: shifting from standardized questions about accuracy to contextual ones about meaning. Comments: Published in Frontiers in Artificial Intelligence Subjects: Artificial Intelligence (cs.AI); Computers and Society (cs.CY) Cite as: arXiv:2604.16403 [cs.AI]   (or arXiv:2604.16403v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.16403 Focus to learn more Journal reference: Front. Artif. Intell. 9:1753041 Related DOI: https://doi.org/10.3389/frai.2026.1753041 Focus to learn more Submission history From: Cody Kommers [view email] [v1] Tue, 31 Mar 2026 12:18:56 UTC (37 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.CY References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)