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Paper short abstract

Public discourse often treats AI as a unified, high-stakes object of controversy. We propose deflationary metascience as a counter-strategy to decompose the AI monolith into precise controversies grounded in concrete contexts, enabling a more democratic scrutiny of AI innovation and policy.

Paper long abstract

Public and policy discourse around artificial intelligence often treats ‘AI’ as a unified, high-stakes object of controversy (Suchman, 2023). We put this assumption to the test by applying controversy mapping methods to scientific discourse and visualising it, revealing a landscape that is fragmented and largely uncontroversial.

Building on a semantic and visual analysis of over two million abstracts on AI, algorithms, and machine learning, we show that algorithms mostly appear as solutions to specific problems, while references to AI in general and explicit controversies are marginal. This finding lies at the base of the Grounding AI Map, a 100m² walkable visualisation of AI-related scientific literature, exhibited at the Danish Technical Museum and Forum Groningen.

The map performs deflation in practice: instead of inflating the AI entity as a singular controversial matter, it systematically decomposes it into thousands of situated, often mundane applications, from medical diagnostics to bus arrival predictions. The exhibition design reinforces this move by denying visitors an overview from above, and requiring interpretation of local clusters by physically moving through them and reading about them. Here, the audience never encounters “AI as such”.

Deflation here is not intended as a depoliticising gesture, but a strategy for metascience: by first breaking down “AI” into its infrastructural roles across fields, controversy mapping can open up more precise, situated, and democratic questions for innovation policy and public engagement.