Algorithms have come to play a critical role in governing our lives. Tech giants, government agencies, NGOs and many other actors have been turning interactions into data, targeting and shaping preferences and behaviors across a range of social settings. Life chances and opportunities increasingly rely on systems that allocate jobs, loans, credit, housing, insurance, welfare, justice, and education based on a range of computationally generated metrics, scores, and rankings. These types of datafied and algorithmic governance have been criticized for their lack of transparency and accountability, exacerbating old and generating new forms of inequality and bias. A central matter of concern has further been the dehumanization of social relations, turning humans into quantified and rationalized data subjects stripped of the richness of their social lives.
In this Ph.D. course, we engage with these scholarly and popular concerns by shifting our perspective from the systems to the people who (have to) live with them. Using analytic sensibilities from Critical Data Studies and Science & Technology Studies (STS), we ask: what happens when we decide to study algorithmic governance through the experiences of experts and citizens? Examining algorithmic systems from the perspective of data scientists, for example, allows us to engage with their own critical conceptions about issues such as bias or future users. Conversely, examining algorithmic systems from the margins, i.e. through the experiences and eyes of the people who are subjected to e.g. credit scores? What folk theories and practices have ordinary people (not experts) developed in the shadow of these systems? How can we make sense of new forms of organizing, mediation, contestation, and resistance adopted by experts and data subjects? What avenues does this suggest for policy, design, and interventions in light of the racialized, gendered, and colonial legacies of many of these systems? How can these new approaches help us rethink established concepts from the field, such as “data ethics”, “data expertise”, “data subjects,” “surveillance,” “gaming,” “participation,” and “resistance”?
Once we begin to examine, through ethnography and other interpretive methods, the different practices that form in relation to datafied and algorithmic cultures, the range of questions and concerns is beginning to expand. Insights from ‘the field’ become important entry points that might help us re-imagine the checkerboard of established critiques and their analytic purchase. A key aim of the course is therefore to provide an overview of analytic possibilities across Critical Data Studies and STS, and to facilitate a reflexive space for thinking about the normativities, practicalities, and possibilities entailed in different approaches to datafication and algorithms.
Read all about the course at https://phdcourses.dk/Course/93547