Evaluating Generative AI for Identifying Ethical, Legal, and Social Dimensions in Migration Narratives: A Case Study of Ukrainian Discourse

Collective endorsement of shared values across diverse social groups is essential for the development and sustainability of democratic societies, yet capturing the perspectives of marginalised populations remains a persistent challenge, particularly when examined through ethical, legal, and social (ELS) lenses. This study develops a structured Migration ELS taxonomy to guide a GenAI-assisted semantic classification model designed to identify ELS dimensions in textual data. The model is fine-tuned and evaluated within a human-in-the-loop framework using expert annotations to ensure reliability and interpretive accuracy. As an empirical case, the approach is applied to migration-related official policy documents and narratives of Ukrainian migrants published on the Telegram platform. The resulting framework enables the analysis of alignment between governmental and migrant perspectives, revealing thematic and temporal divergences in ELS dimensions across institutional and user-generated discourse. The findings demonstrate the potential of this scalable framework, which combines taxonomy-driven modelling with generative AI and expert-in-the-loop validation, to reveal patterns of alignment and temporal dynamics in the representation of values across different social groups.