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Accepted Paper

Making AI Deployable, Making AI Knowable: A Responsive Methodology for Studying Emergent Technology in Motion  
Xiao Yang (University of Edinburgh)

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Paper short abstract

This paper presents a responsive methodology for studying medical AI deployment as an emergent, contested process. Drawing on multi-study research, it reflects on the epistemological, political-economic, and temporal challenges of tracking shifting objects across a turbulent and heterogeneous arena.

Paper long abstract

Studying medical AI deployment poses distinctive methodological challenges. The object of inquiry is not a stable technology but an ongoing accomplishment, continually reassembled by vendors, healthcare institutions, intermediaries, and regulators. Strategies shift mid-research, problem definitions mutate, and the boundaries of "the system" are themselves contested. Traditional STS approaches struggle to capture this moving target, while established framings of platformisation risk importing assumptions of planful disruption that obscure the dispersed, contingent character of what actors actually do.

This paper presents a reflexive, responsive design developed across a multi-study thesis on medical AI deployment. Rather than fixing the research object in advance, the approach adapts iteratively to practitioners' shifting understandings, combining landscape mapping, organisational vignettes, and longitudinal case study to trace deployment as continued formation work, a form of methodological "encircling" that follows how actors borrow, imitate, and mobilise strategies across a heterogeneous arena.

Empirically, the approach reveals dynamics fixed-frame methods would miss: how platformisation becomes multiple as actors enact divergent ontologies, and how a hospital created a "platform of platforms" resisting vendor dependency, contrary to dominant platform narratives. These findings emerged through sustained responsiveness to a turbulent ecology, not predetermined design.

The paper reflects on three entanglements: studying objects whose boundaries are actively contested; navigating layered opacities across vendors, procurement, and regulation; and registering how imaginaries and materialities co-evolve amid hype and volatility.

Traditional Open Panel P043
The matter of method in researching AI: elusiveness, scale, opacity
  Session 3