Synthetic Interlocutors. Experiments with Generative AI to Prolong Ethnographic Encounters
This addresses the challenge of prolonging ethnographic fieldwork interactions for researchers, though it appears incremental as it applies existing RAG methods to a new domain.
The paper tackles the problem of extending ethnographic analysis by introducing 'Synthetic Interlocutors', chatbots using Retrieval Augmented Generation (RAG) with ethnographic data, and finds that they can digest materials and lead to prolonged, uneasy encounters that facilitate novel insights.
This paper introduces "Synthetic Interlocutors" for ethnographic research. Synthetic Interlocutors are chatbots ingested with ethnographic textual material (interviews and observations) by using Retrieval Augmented Generation (RAG). We integrated an open-source large language model with ethnographic data from three projects to explore two questions: Can RAG digest ethnographic material and act as ethnographic interlocutor? And, if so, can Synthetic Interlocutors prolong encounters with the field and extend our analysis? Through reflections on the process of building our Synthetic Interlocutors and an experimental collaborative workshop, we suggest that RAG can digest ethnographic materials, and it might lead to prolonged, yet uneasy ethnographic encounters that allowed us to partially recreate and re-visit fieldwork interactions while facilitating opportunities for novel analytic insights. Synthetic Interlocutors can produce collaborative, ambiguous and serendipitous moments.