Special issue on machine anthropology
Bringing together a motley crew of social scientists and data scientists, the aim of this special theme issue of Big Data & Society 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 thought: are we about the 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.
A selection:
The Thick Machine: Anthropological AI between explanation and explication
By Anders Kristian Munk, Asger Gehrt Olesen, Mathieu Jacomy (The Techno-Anthropology Lab, Aalborg University Copenhagen)
> Note the upcoming public lecture by Anders Munk on 17 June (via Zoom)
A view from anthropology: Should anthropologists fear the data machines?
Kristoffer Albris, Eva I Otto, Sofie L Astrupgaard, (Copenhagen Center for Social Data Science, University of Copenhagen)
Computational ethnography: A view from sociology
Phillip Brooker (University of Liverpool)
Ethnographic data in the age of big data: How to compare and combine
Andreas Bjerre-Nielsen, Kristoffer Lind Glavind (Department of Copenhagen Economics, University of Copenhagen)
