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Paper short abstract:

Considered as performance instead of science, AI embedded in small devices provides partial perspectives and incomplete knowledge, shifting attention from functionality to actors and actions, thus suited to ethnographic study. Conversely AI becomes a participant-observer of human cultures itself.

Paper long abstract:

My research is motivated by two drivers of change in AI research: a need for ethnographic approaches, and increasing technical development pushing AI into the edges of the network.

Considered as literature, craft or performance instead of a science makes AI particularly suited to ethnography. This shifts attention from the uses and outcomes of an AI deployment to the actors involved. Rapid technical development of AI, in parallel with shrinking hardware and faster processing speeds, means that both sensing and processing can take place at the edges of a network, not only in centralised servers. Accordingly, AI becomes an internal quality of things instead of their external supervisor: lightweight devices each provide partial perspectives and incomplete knowledge, together having the potential for emergent collective intelligence which incorporates both human and nonhuman actors.

More broadly, this means that AI is increasingly and invisibly embedded in natural and cultural systems. This opens up opportunities to study empirically the theory that intelligence can be seen a feature of an ecosystem. What happens when a nonhuman participant is an informant and a collaborator in the construction of knowledge?

When AI is embedded deeply and invisibly in human cultures, it could enable new perspectives on such cultures. There is growing discussion about post-humanist approaches in HCI and anthropology, but this is so far almost entirely theoretical, drawing from science fiction; a pervasive AI deployment, developed and analysed using an ethnographic approach, raises the possibility of evaluating such an approach empirically.