Paper short abstract:

We present the experiment of creating an atlas of algorithms to facilitate a more democratic and less hyped engagement with AI. We perform AI as mundane in two ways: by exposing the extent to which it gets blackboxed in the context of science; and by using it as a pragmatic research companion.

Paper long abstract:

In her recent commentary questioning “the uncontroversial thingness of AI”, Lucy Suchman

(2023) argues for the need to ask more mundane questions about specific algorithms in

concrete situations. What are they doing? Should they be doing it? Could it be otherwise?

Reifying AI as a technology only contributes to the hype and prevents a better democratic

engagement with the myriad of issues that arise in diverse socio-technical circumstances.

In this paper we reflect on how to make machine learning algorithms boring again, in the sense

of counteracting hype narratives, be they celebratory or doom-and-gloomy. To do so, we build a

map of what algorithms are doing in the scientific literature, complete with qualitative

annotations exposing their purpose and agency across a wide range of situations.

Carrying out this annotation project on such an extensive corpus entailed collaborating with a

large language model, to summarize sets of highly specialized scientific abstracts in a manner

that is intelligible to a non-expert audience.

We thus perform AI as doubly mundane. As a technology made invisible by its own success, in

the context of scientific publications, as displayed by the atlas; and as a pragmatic means to

translate specialized documents into relatable annotations, a coding that a human agent could

carry out better but not at such a large scale (thousands of summaries).