Format: hybrid (In-person and Online)
Language: russian
Mail: another.ai.section@gmail.com
Organizers:
Aziz Ashirov, 2nd-year PhD Student, ITMO University, Philosophical Anthropology

Ilya Uryadnikov, 1st-year PhD Student, ITMO University, Philosophical Anthropology

Maria Keshishyan, MA Graduate, Staff Member, ITMO University

Artificial Intelligence has become a central theme in both academic and public discourse. However, the intensity of the discussion is not always matched by the depth of reflection. Between technological enthusiasm and alarmism, there remains a space for systematic empirical research on AI as a sociotechnical phenomenon.
The section’s title captures this situation: indeed, there are many AI sections. Our task is not to add yet another discussion about the potential or threats of the technology, but to bring together researchers studying AI critically and empirically—through specific practices, contexts, and cases.

By "critical," we do not mean a negative assessment, but rather an analytical lens that allows us to see the social within the technical: who creates AI systems and how; what practices emerge around them; whom they include and exclude; and how the infrastructures making AI possible operate. Doing so, we draw on the tradition of critical data, algorithm, and infrastructure studies (Bowker & Star, 1999; Kitchin, 2014; Benjamin, 2019; Couldry & Mejias, 2019). Here, "critical" implies attention to those hidden mechanisms, assumptions, and practices built into technical solutions that shape their social consequences, as well as the search for ways to make these processes visible and accessible for analysis and discussion.

The section invites researchers who study AI “in the wild" — researchers working with AI empirically, in the field. We aim to gather methodological and empirical experience in studying AI within the Russian-speaking context and juxtapose it with international practices. We are interested in ethnographies of developers and users, analyses of interfaces and datasets, studies of infrastructures and labor practices, and methodological experiments in studying algorithmic systems.

The goal of the section is to create a space for exchanging methods and results, and for discussing how to research AI systems without reproducing industrial narratives about them. We strive for a conversation where different disciplinary traditions and research practices meet.

• Look How They Do It:

Experience of international critical AI studies and possibilities for their adaptation.

• Look What I Can Do:

Methodological experiments and case studies by researchers.

• Wait, You Can Do That?:

Non-standard approaches to studying AI systems.

• How long?:

Critical analysis of the industrial discourse on AI and its consequences.

• A Lockpick for the Black Box:

Methods for researching opaque algorithmic systems.

*We welcome papers based on approaches from Science and Technology Studies (STS), Human-Computer Interaction (HCI), Critical Data/AI Studies, Digital Anthropology, and related fields. Work that challenges established concepts (strong/weak AI, explainability, agency) through specific empirical material is highly encouraged.