Course Living with Algorithms: Power, Practices, and People in a Datafied World

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, we may also choose to examine 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. 

Aim:

The course will provide the participants with:

a) an introduction to key scholarly debates across science & technology studies (STS) and critical data studies about how to understand and analytically engage with questions of datafication and algorithmic power;

b) an opportunity to engage first-hand with empirical materials and how they might help us to rethink themes of critique, practice, and subject-formation, as they are deployed across Critical Data Studies and STS;

c) an opportunity to examine how these ways of thinking may (or may not!) be applied to the participants’ own research projects. 

Upon completion of the course, participants will have:

• achieved a strong reflexivity regarding the choice of methods, theories, and concepts, and how these foreground or erase specific questions, problems, and normativities in the study of computational technologies;
 
• acquired awareness of different ethnographic and conceptual entry points for reconsidering the powers of algorithms and data. This awareness will be exemplified by themes of web search machines, public administration, credit scoring, and participants’ own projects. 

• increased participants’ critical ability to account for the potential role of STS in combination with critical data studies, in general, and how it is applied in the participant’s research, specifically. 

Literature:

Course readings (600-800 pages) will be specified closer to course date.

Target group:

Only PhD students can participate in the course.

Language:

English

Form:

In terms of format, the course combines a mix of analytical approaches that will help us learn collaboratively and creatively. Based on an initial set of readings and reflections, we will engage in lectures and discussions; data and paper feedback sessions, in which we make sense of fieldnotes, interviews, and artefacts provided by participants; an algorithmic walk through the city of Copenhagen; writing activities and workshops that help us develop and reflect on our own voices in research process. 

Lecturers:

Helene Friis Ratner, DPU – Danish School of Education, Aarhus University

Malte Ziewitz, Department of Science & Technology Studies, Cornell University 

Venue:

Campus Emdrup, Tuborgvej 164, 2400 Copenhagen NV. - room A410

Course dates:

• 12 December 2022 09:00 - 16:30
• 13 December 2022 09:00 - 16:30
• 14 December 2022 09:00 - 16:30