On Studying Algorithms Ethnographically: Making Sense of Objects of Ignorance

In this article, we make sense of financial algorithms as new objects of concern for organizational ethnography. We conceive of algorithms as ‘objects of ignorance’ jeopardizing traditional ethnography from the perspective of its categories and methods. We investigate the organizational politics taking place within high-frequency trading – a sub-field of algorithmic trading where automated decision-making without human direction has reached a peak, and show that financial algorithms raise particular epistemic and methodological challenges for practitioners and ethnographers alike. Consequently, we develop a typology for various interpretations of algorithms as ethnographic objects, accounting for their structural ignorance and shedding light on a continuum of the changing human-machine/trader-algorithm relation. To this end, we use the concepts of ‘quasi-object’ and ‘quasi-subject’ as developed by Michel Serres, and make the point that in order to study financial algorithms ethnographically, we need to think anew the dynamic relationship they embody, and acknowledge their constitutive heterogeneity.