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Paper short abstract

Taking inspiration from classic STS studies - and arguing critically against current approaches of media studies and critical data studies - the paper outlines a methodology for studying AI governance and society tailored for a study of the Danish government’s taskforce for AI in the public sector.

Paper long abstract

The authors of this paper conduct a 2-year study of ’the digital taskforce for AI’, a group of fourteen civil servant who have been given the task of orchestrating the roll-out of large-scale AI solutions in the Danish Public sector. It is widely expected that the taskforce will play a key role in defining meaningful AI ’solutions’ and meaningful applications in the years to come.

Unsurprisingly, we have faced some problem of access, but the most difficult challenge has been to define a methodological approach that does justice to the phenomenon of a welfare state struggling to make good use of AI amidst geo-political tensions, rapid technological development and public controversies about AI. This tumultuous phenomenon, we argue would be difficult to get into focus if we followed the analytical styles currently developed by STS in dialogue with fields such as critical data studies and media studies. Particularly problematic to our case, we suggest, is the tendency of critical data studies to focus on victims rather than (also) the imperfect attempts to compose common goods. Equally problematic is STS/media studies’ preoccupation with studying semantic patterns across vast media datasets, which in our view jettisons the opportunity of developing stronger historical-narrative explanations of critical decisions and path-dependencies.

The paper presents essential details of our methodological program and its inspiration from classic STS studies of the co-construction of technology and society. We argue that STS has a rich classic toolbox of methodological resources that could and should be mobilized for studying AI.