Abstract

We explore multi-scale relations in artificial intelligence (AI) use in order to identify difficulties with coordinating relations between users, machine learning (ML) processes, and “sociobuilt contexts”—specifically in terms of their applications to medical technologies and decisions. We begin by analyzing a recent COVID-19 machine learning case study in order to present the difficulty of traversing the detailed causal topography of “sociobuilt contexts.” We propose that the adequate representation of the interactions between social and built processes that occur on many scales ought to drive interdisciplinary approaches for ML modification. Next, we describe ML algorithm development as a process that is partly dependent on methodological stabilization for reliability and partly on coordinating relations. In the coordination between user, ML process, and sociobuilt contexts, we propose that new methods can be explored that promote the inclusion of patients and communities for the purpose of cross-checking portions of the ML process. Finally, we suggest that the advantages of responsible innovation emerge through the iterative entanglement of ethical, methodological, and ontological considerations within the broader conceptual infrastructure of epistemic responsibility.