Short abstract:

This contribution draws on an ethnography of a computer science lab in Romania and argues that the stories, or fables (Haraway 2016), told both within the lab and about it are key to understanding algorithmic decisions-making and its inherent societal and political consequences.

Long abstract:

With the increased role of machine learning in security applications, questions about the interpretability of AI are gaining relevance in both computer and social sciences. This contribution draws on an ethnography of a computer science lab in Romania, where software engineers work on the interpretability of image recognition algorithms. It argues that the stories, or fables (Haraway 2016), told both within and about the lab are key to understanding algorithmic decision-making and their inherent societal and political consequences. In the lab, tinkering with deep neural network models, introducing additional layers into the learning process, and creating “visualisations”, such as heat maps, is not only a technical process but also, at every stage, relies on and incorporates storytelling. Computer scientists often use stories and metaphors, through which fabulation is entangled with the material and technical practices of “making”. In addition, fabulation is entangled with the practices of knowledge production about such practices within STS. Through an innovative methodological approach, this paper draws on a collaboration and co-laboration between a social- and computer scientist, and mobilises multimodal methods to argue that different modes of storytelling might enhance our understanding of “black boxed” image recognition algorithms and their societal and political consequences.