Ethnography (social scientific methods that illuminate how people understand, navigate and shape the real world contexts in which they live their lives) and machine learning (computational techniques that use big data and statistical learning models to perform quantifiable tasks) are each core to contemporary social science. Yet these tools have remained largely separate in practice. This chapter draws on a growing body of scholarship that argues that ethnography and machine learning can be usefully combined, particularly for large comparative studies. Specifically, this paper (a) explains the value (and challenges) of using machine learning alongside qualitative field research for certain types of projects, (b) discusses recent methodological trends to this effect, (c) provides examples that illustrate workflow drawn from several large projects, and (d) concludes with a roadmap for enabling productive coevolution of field methods and machine learning.

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.

Although I am usually tongue tied and fluttery of stomach when I do public talks, I do definitely enjoy the opportunity to engage an audience with questions around AI and robotics. As an anthropologist, I am trained in watching reactions in assemblies of communities; being a research instrument that interrogates a field site of informants, But often it is when I am the one being interrogated by the audience during the Q&A when I have the best moments of understanding and inspiration. Finding out what people want to ask about is about more than just finding out what is weighing heavily on their mind. It is also about learning how their own unique mind has approached a particular topic. For instance, a couple of weeks ago I was speaking at the Hay Festival, my first time there, and a 10 year old boy in the audience asked a question that showed me how his mind was thinking things through.