Bridging AI and the Humanities: Explainable Foundation Models for Historical and Subjective Data

Keynote by John Pavlopoulos, Athens Universityy of Economics and Business

Abstract: Foundation models are transforming AI research, yet their application in the humanities remains underexplored. These large-scale models, trained on diverse corpora, hold immense potential for analyzing historical texts, cultural artifacts, and long-term societal trends. However, their applicability to humanistic inquiry is limited by three key challenges: (1) the temporal drift of language and meaning across centuries, (2) the subjectivity of interpretation in disciplines where multiple perspectives coexist, and (3) the lack of explainability, making AI-generated insights difficult for scholars to trust or critique. This talk explores strategies to adapt foundation models for humanities research. We discuss approaches for handling chronologically distributed data and we examine how multi-annotator learning can embrace disagreement in interpretation—crucial for fields where diverse viewpoints enrich analysis rather than distort it. Additionally, the talk will highlight techniques for making AI-generated insights transparent and interpretable, such as explainability, confidence estimation, and human-in-the-loop validation, ensuring that AI remains a collaborative tool rather than a black box. By adapting foundation models to historical and cultural contexts, we can develop AI systems that are not only powerful but also responsible and aligned with humanistic research values. This talk aims to bridge the gap between state-of-the-art AI and humanities scholarship, demonstrating how interdisciplinary collaboration can unlock new insights into our past and present.

Section 4: Bridging AI and the Humanities: Explainable Foundation Models for Historical and Subjective Data