Stefania Milan is Associate Professor of New Media and Digital Culture at the Department of Media Studies, University of Amsterdam, and a Faculty Associate at the Berkman Klein Center for Internet & Society, Harvard University. Her work explores the interplay between digital technology, activism and governance. Stefania is the Principal Investigator of the DATACTIVE project, funded by the European Research Council and exploring the evolution of activism and political participation in the Datafied Society (data-activism.net), and Co-Principal Investigator in the Marie Curie Innovative Training Network “Early language development in the digital age” (e-ladda.eu). She is also the Project Leader of 'Making the hidden visible: Co-designing for public values in standard-making and governance’ (IN-SIGHT), funded by the Dutch Organization for Scientific Research (NWO) as part of its Responsible Innovation programme (in-sight.it). Stefania is the author of ‘Social Movements and Their Technologies: Wiring Social Change’ (Palgrave Macmillan, 2013/2016), co-author of ‘Media/Society’ (Sage, 2011), and co-editor of ‘COVID-19 from the Margins: Pandemic Invisibilities, Policies and Resistance in the Datafied Society’ (Institute of Network Culture, 2021). She enjoys experimenting with digital and action-oriented research methods and finding ways to bridge research with policy and action. Outside office hours, she loves mountaineering, boxing and cycling.
Digital Publics in the Age of Data Capitalism
Data capitalism has dramatically changed the role of information and technology in the constitution of the social. Its business model—the transformation of human actions, interactions and emotions into data points which can be analyzed and monetized—has accelerated the crisis of liberal democracy. Its global reach has contributed to alter power relations and has introduced novel forms of colonialism and exploitation of resources.
This talks surveys three building blocks of data capitalism and their effects on digital publics, namely: i) personalization algorithms and the polarization of the public sphere they induce, ii) AdTech, or the technological ecosystem supporting targeted advertising, and the lack of transparency and accountability surrounding this market, and iii) facial recognition technology, as one of the most widespread yet most intrusive applications of Artificial Intelligence, and its implications for human rights including privacy. It dissects the environmental costs of data capitalism, and explores potential responses and forms of resistance to intrusive technology from the bottom-up, focusing on data activism as the generator of novel imaginaries and innovative practices of civic engagement.
The Datafied Society
Today notions like big data, smart city and artificial intelligence (AI) are frequently evoked in the narratives of the industry and policymakers alike. They yield the promise of efficiency, empowerment, and a better life. Yet, they are not free from risks for privacy and citizen agency.
“The Datafied Society” explores the theoretical frameworks that allow us to capture and interpret the technological changes at the core of contemporary society and their societal consequences. The course has four components:
- Theorizing the datafied society, defining the interdisciplinary theoretical toolbox to study society at the age of AI;
- Political agency in the datafied society, where we will analyze, e.g., the evolution of contemporary social movements;
- Decolonizing data studies, investigating non-Western approaches to the study of the datafied society, and
- Methods for algorithmic accountability, where we will look at innovative methods to study the datafied society and the theory implications of this type of research.
Upon completion of this course, you will be able to understand the datafied society and critically evaluate its consequences on political agency, describe the opportunities and challenges for citizens on the basis of theory and concrete examples, deconstruct mainstream theoretical approaches, and reflect on the methodological challenges of studying algorithmic-mediated phenomena. This course will be taught in English and engaged participation is expected. Readings will be made available through ILIAS.