Practice based research, currently in progress at the Information Experience Design department of the Royal College of Art, on a scholarship from Microsoft Research Cambridge, UK.
Supervisors: Kevin Walker (RCA), Richard Banks (Microsoft)
Working title and abstract:
This PhD thesis investigates diagrams as a language for research and practice to critically investigate algorithmic prediction. My practice-based research uses this language to read algorithmic prediction as a set of intricate computational geometries, and to write it through critical practice immersed in the very materials in question: data and code. I frame this with a methodology informed by media archaeology, specifically through the notion of excavation from the technical and historical "depths" of algorithmic systems.
I explore how diagrams can be used as a language to critically investigate algorithmic prediction through design practice, with two secondary research questions:
Through which mechanisms does thinking/writing/designing in diagrammatic terms inform research and practice focused on algorithmic prediction?
As algorithmic systems claim to produce objective knowledge, how can diagrams be used as instruments for speculative/conjectural knowledge production?
This research is contextualised by three registers of relations between diagrams and algorithmic prediction. Data diagrams describe the algorithmic forms and processes through which data are turned into predictions. Control diagrams afford critical perspectives on algorithmic prediction, framing the latter as an apparatus of prescription and control. Speculative diagrams open up opportunities for reclaiming the generative potential of computation.
I demonstrate uses of diagrams as a language in three practice chapters, each focusing on one diagrammatic form. With the case board I unpack some of the historical genealogies of algorithmic prediction, with a purpose-built application that materialises broader reflections about how such genealogies might be conceptualised, facilitating a visual and subjective mode of knowledge production. I then move to producing traces, namely probing the output of an algorithmic prediction system—in this case YouTube recommendations. Traces, and the purpose-built instruments used to visualise them, interrogate both the mechanisms of algorithmic capture and claims to make these mechanisms transparent through data visualisations. Finally, I produce algorithmic predictions myself, and examine the diagrammatic "tricks," or chicanes, that this involves. I revisit a historical prototype for algorithmic prediction, the almanac, and use it as a site to question the boundaries between data science and divination. This is materialised through a new version of the almanac—an automated publication where the tools of algorithmic prediction are used as part of divinatory rationalities.
My original contribution to knowledge is an approach to practice-based research which draws from media archaeology and focuses on diagrams, to investigate algorithmic prediction through design practice. I demonstrate to researchers and practitioners with interests in algorithmic systems, prediction, and/or speculation, that diagrams can be used as a language to engage critically with these themes.