Editorial introduction: Towards a machinic anthropology

Abstract

Bringing together a motley crew of social scientists and data scientists, the aim of this special theme issue is to explore what an integration or even fusion between anthropology and data science might look like. Going beyond existing work on the complementarity between ‘thick’ qualitative and ‘big’ quantitative data, the ambition is to unsettle and push established disciplinary, methodological and epistemological boundaries by creatively and critically probing various computational methods for augmenting and automatizing the collection, processing and analysis of ethnographic data, and vice versa. Can ethnographic and other qualitative data and methods be integrated with natural language processing tools and other machine-learning techniques, and if so, to what effect? Does the rise of data science allow for the realization of Levi-Strauss’ old dream of a computational structuralism, and even if so, should it? Might one even go as far as saying that computers are now becoming agents of social scientific analysis or even thinking: are we about to witness the birth of distinctly anthropological forms of artificial intelligence? By exploring these questions, the hope is not only to introduce scholars and students to computational anthropological methods, but also to disrupt predominant norms and assumptions among computational social scientists and data science writ large.

Anthropology and complicated people

“…In the post–World War II era, interest in complexity grew. Lesley White (1959), for example, resuscitated the idea that society progressed from simpler to more complex forms, while Lévi‐Strauss introduced a different kind of complexity to social theory with his computational structuralism (Pedersen, 2023).…”

Special Issue Editorial Introduction: Entangling Data while Entangling Disciplines

“…In an attempt to go a step further, Axel Morten Pedersen (2023), in his introduction to a special issue on 'machinic anthropology', has summarised anthropological scholarly production on data in three main categories: anthropology of big data, anthropology with big data and anthropology as data science. The first category includes works that critically analyse data scientists' practices, the second category moves beyond a simple critique in engaging with data scientists and the last category, the focus of Pedersen's special issue, hints at conceptualising anthropology as a kind of data science in an attempt to realise Levi-Strauss' old computational dream, a 'distinctly anthropological forms of machine learning and AI' that also opens up 'for a fusion between the radically empiricist commitment of much contemporary anthropology and sociology's continuing commitment to big theory building' (Pedersen 2023: 5-6).…”