Send message to Author

Short abstract:

In this presentation, I describe the methodological development and ongoing puzzles of a large research programme designed to understand and theorise the changing relations of data, care and learning at multiple scales in informatics-informed medicine.

Long abstract:

What kinds of work are required to produce learning in healthcare at the intersection of clinical care, research and informatics? How are data practices and care related to each other in this new configuration? And how are new forms of data and care work constituted by and constitutive of the specificities of time, place and personhood? These questions animate a five-year study of data and care practices in the era of biomedical AI, which I describe in this presentation. How to answer these questions ethnographically across multiple scales is the puzzle I address. Ethnographies of data are keenly advocated for in social studies of medical AI, but remain few and far between. Big data’s ‘mercurial’ character, resulting both from its ubiquity and polysemy, demands that we foreground its specificity, putting new instantiations of data practices into conversation with long-standing theoretical concerns.

The research takes a multiscale ethnographic approach in order to understand developments in data|care practices, including machine learning and AI, which operate across different dimensions of lived experience, from the home to the hospital to the nation state. We adopt a practice-based approach, starting from the premise that these practices are sociotechnical, situated, contingent and performative. In this presentation I delve into the challenges and opportunities this poses, considering issues such as scale, comparison, and how to capture the sensory. In so doing, I contribute to debates about how to rethink the conceptual and methodological repertoires STS uses to engage with medical AI.