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The Digital Infrastructure Incubator at Code for Science & Society is hosting a series of public events on "Political Imagination to Support and Sustain Digital Infrastructure":

These discussions connect conversations on sustainability, governance, and community health in digital public infrastructure to wider political horizons. Invited panelists draw from their experiences in unionization drives, climate actions, abolition movements, among others. Together these events draw out interdisciplinary resonances and invite participants to make connections to neighborhoods and communities on and off line.

The next event on February 23, 2022, is on "Coloniality of Digital Infrastructure."

Meredith Whittaker takes on corporate AI in the ACM’s Interactions:

Modern AI is fundamentally dependent on corporate resources and business practices, and our increasing reliance on such AI cedes inordinate power over our lives and institutions to a handful of tech firms. It also gives these firms significant influence over both the direction of AI development and the academic institutions wishing to research it. Meaning that tech firms are startlingly well positioned to shape what we do—and do not—know about AI and the business behind it, at the same time that their AI products are working to shape our lives and institutions.

The peer-reviewed journal Big Data & Society has begun publishing contributions to its "Machine Anthropology" special issue edited by Morten Axel Pedersen.

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: 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.

A group of junior anthropologists has contributed "A View from Anthropology: Should Anthropologists Fear the Data Machines?"

If you are an anthropologist wanting to use digital methods or programming as part of your research, where do you start? In this commentary, we discuss three ways in which anthropologists can use computational tools to enhance, support, and complement ethnographic methods. By presenting our reflections, we hope to contribute to the stirring conversations about the potential future role(s) of (social) data science vis-a-vis anthropology and ethnography, and to inspire other anthropologists to take up the use of digital methods, programming, and computational tools in their own research.