This paper presents some findings of the interdisciplinary project ‘Algorithmic Identities.’ This project was devised to study how people feel, react and thematise the extraction of digital data and algorithmic inferences about their personhoods. Considering the proprietary, opaque and inscrutable algorithmic systems of major online services and social media platforms, we adopted a critical making approach to doing research: we made an app that emulates profiling and recommendation systems. “Big Sister”, as we called it, takes data from social media accounts or user-written text to generates a user profile of personality traits and consumer preferences as well as music recommendations based on this profile. Through this app the user can experience how is predicted and play with the algorithmic inferences, but also the Big Sister app acts as an instrument for research, allowing us to explore and elicit participants’ experiences of the app and their relationship to algorithmic profiling and recommendation systems in general. Through an open call, we invited participants in Chile and the United Kingdom to use Big Sister and to be interviewed about their experience. Using the fresh qualitative method known as “trace interviews”, we examine how people understand, inhabit and shape algorithms through habitual use and in turn are shaped by these algorithms in their everyday life. And at the same time, the development of Big Sister and the question of how to maintain engaged the user provoked technical, epistemological, legal and ethical issues, forcing us to re-thought some of our own methodological assumptions and goals.
This is an abstract for the EASST/4S 2020 open panel "Digital Experiments in the Making: Methods, Tools, and Platforms in the Infrastructuring of STS".