Anthropology in Action

This special issue discusses forms of possible collaboration and mutual intermixing between anthropology and data science, by presenting projects and creative experiments that have been conducted astray the two fields. While we may say that all scientists work with data, this special issue focuses on data that are collected and/or processed by digital means. In addition, attention will be paid to computation as anthropologists have recently turned to the study of data, AI and algorithms, offering critical insights about their production and implementation. They have addressed the effects of algorithmic automation (e.g. increasing surveillance, inequality exacerbation, new forms of discrimination) and conducted fieldwork among data scientists in order to bring the socio-cultural dimensions of their work to the forefront. In this introduction, we will illustrate what motivated this special issue and will introduce the articles by positioning them critically within the current debate about computation, big data and AI.

Based on our experiences from ongoing collaborations with computational engineers over the course of six years and two interdisciplinary research projects, with this article we suggest that the building of collaborations between anthropology and computational sciences that alter disciplinary boundaries and bridge epistemic differences can be accomplished through three levels of engagement: a shared research project, becoming involved in each other's theoretical universes, and crafting physical spaces for shared intellectual practice. Taking an empirical point of departure in our colleagues’ attempt to cross the methodological and epistemic divide between engineering and anthropology through game theory, we introduce how the distinction between the ideal, the real and the actual serves not only as a model of our collaborations but also as a generalisable model for future collaborations across anthropology and computational sciences.

The digitisation of agriculture is underway—with advocates highlighting its potential to improve resource efficiency and reduce environmental impacts of agriculture, and critics suggesting that emerging technology will exacerbate inequalities in the food system. Drawing on five years of collaboration within an interdisciplinary sustainable agriculture project, I critically consider the limitations of on-farm research as a strategy for rendering ‘real-world’ farm practice into data for digital tools to guide farmer decision-making. In engagements with data science, anthropologists must use our seat at the table to practice what Helen Verran (2001) called ‘generative critique’ in support of responsible research and innovation. Our training in engaged yet critical participant observation, and our experience building long-term relationships within communities, makes anthropologists particularly well-suited for this role.

What do fieldwork experience in Indonesia and working at a computing company have in common? Why does it make sense for an anthropologist to work at a computing company? What do anthropologists and data scientists have in common? In this article, I will attempt to answer these questions and show how they are all interrelated. Additionally, I will explore how anthropology can impact data science and how data science can affect anthropology in the context of multidisciplinary approaches, similarities, innovation, and languages, along with how the awareness of the complementarities of the two disciplines influences collaboration and development of knowledge.

This work intends to describe the humanitarian response to the cholera epidemic carried out by the international health non-governmental organisation Doctors with Africa CUAMM in the provinces of Zambezia, Sofala and Tete in Mozambique. The knowledge and practices of applied medical anthropology have proved to be fundamental in the management of prevention and monitoring activities in homes where cases of cholera have been identified and confirmed. The collection and management of data relating to the epidemiological curve has represented a challenge in the field and pose considerable theoretical problems with respect to the representation, public discourse and policies of local government and large international donors in response to health emergencies caused by the ecological crisis-climate, especially in Africa and Mozambique.

Anthropologists have yet to fully consider how artificial intelligence (AI) and related technologies may be put to work to develop novel ethnographic media that transcend the basic limitations of the traditional ethnographic text. This article describes an experiment in the development of an AI system based on ethnographic fieldwork focusing on a specific form of nonverbal interaction and human practitioners’ evaluations of the system's ‘humanness’. Among many other elements of humanness, this discussion highlights the many ways that human subjects locate humanness in an agent's ability to parse and respond to others in the midst of nonverbal social interaction.

This special issue draws from the AAA panel ‘Entangling data while entangling disciplines: discussing the future of anthropological collaborations with data scientists’. It deals with experiences of anthropologists who have collaborated with data scientists. To render the panel truly interdisciplinary, the organizers of the AAA panel invited a data scientist as discussant, Katie Amato. Below her comments have been elaborated in the form of a dialogue with anthropologist Roberta Raffaetà. This afterword aims to sketch some paths of reflections about what data scientists think of collaborations with anthropologists.