Future Directions of Critical Data Studies

From the beginning, the panel had a shared understanding that Critical Data Studies would include or touch upon the inquiry of platforms, algorithms, and data. Informed by software studies, media studies, and the science of technology studies, the emerging Critical Data Studies seem inherently to combine qualitative empirical research methods with digital methods and data analysis. Because datafication affects almost all areas of society and requires participation from, among others, computer science, law, media studies, sociology, statistics, geo sciences and urban studies, etc., interdisciplinary collaboration is essential to understanding the ongoing transformation and to address the urgent issues raised by it. Stefania Milan emphasized the importance of looking at big data not only from the rapidly digitizing economies of former industrial societies but from a perspective of the global south. The work of her Data-Active research group critically inquires into data collection and analysis, mapping the impact on citizenship and seeking exploring meaningful applications to further civil rights. Lina Dencik from the Data Justice Lab presented issues that emerge from datafication and algorithmic processing. The group investigates the politics and impacts of data practices and how they constitute injustice. Tarleton Gillespie emphasized the increasing automation through algorithms in knowledge production and information processing and the need to empirically understand and conceptually capture the implications for society.

At Data School and the Datafied Society Research Platform we feel an increasing affinity with the emerging field of Critical Data Studies. We used the opportunity of the AoIR roundtable to summarize our thoughts and to define a distinct perspective. In our understanding, Critical Data Studies is inherently inter- and transdisciplinary. It is grounded in digital Bildung (Berry 2012; Rieder and Röhle 2017). Based on empirical research and informed by practical use and application, Critical Data Studies should improve data literacy, data practices and policy making. These are the basic guidelines shaping our activities at Data School and the Datafied Society Research platform.

Inside Datafication

Our research takes place in and with the field. Driven by our research interest into datafication and how it affects citizenship, participation in deliberative processes, the public sphere and cultural production, we conduct our research within the societal domains of public management, (public) media and the public space. Using our own practice of entrepreneurial scholarship, we developed services and products that are useful for partners within said domains.

Entrepreneurial scholarship here must not be confused with academic entrepreneurship. While academic entrepreneurship utilizes research findings for commercial activity, entrepreneurial scholarship uses commercial services and products to facilitate deep engagement with the domain of the research subject. The commercial activity is an ‘anthropological vehicle’ that allows the researchers to gain an inside view.

Working side by side with practitioners in the field provides rich data and an intimate inside view on how datafication manifests, which narratives shape the expectations in data practices, which actors affect the development of data-driven solutions, etc. Data School focuses mostly on teaching data analysis and carrying out applied research projects. The Datafied Society Research Platform connects scholars from different disciplines, making an interdisciplinary effort to conceptualize the datafied society.

Teaching in the Field

At Data School, our version of the ‘flipped classroom’ is a perpetual field trip. Students from different disciplines work for and with our external partners on actual data issues: Learning how to analyse data, and using different tools and methods, our students combine the qualities of their various study programs with new competences for tackling a variety of research questions. Most importantly, they gain invaluable experience and insights from working directly with practitioners. They also meet possible future employers and experience the dynamic of professional work environments. Our classes are not only accessible for students from all disciplines, but also for professionals who make up approximately 10% of each student cohort. Their participation enriches our course with valuable experience and different perspectives. In addition, the Data School teaches directly in the field, providing courses on data literacy and data skills for public management employees and other professionals.

Tool Criticism

In line with the argument made by Rieder and Röhle (2012) in their “Five Challenges for the Digital Humanities”, our students and researchers consider the epistemological impact of knowledge technologies. This implies not only using, but critically inquiring into the software, the various interfaces, the algorithms and the processes that make data analysis possible. It starts with teaching students to assemble a data set to learn about the many decisions involved – from capturing data over processing and analysing to visualizing results.

Impact

Our research findings should directly inform data practices and policy-making, as well as further the development of practical applications. Where possible, we develop products for academic and societal application. An example would be our Data Ethics Decision Aid (DEDA) which was developed in close cooperation with the City of Utrecht and their data analysts, project managers, privacy officer and information advisors. DEDA is now used by practitioners in the field and also serves as a research tool to gain an inside view into data projects and ethical awareness in different sectors. Another example would be our Data School for Public Management which trains public servants in data practices but also discusses the various socio-political issue raised through datafication. Referring to our notion of tool criticism, we also strive to have an impact in epistemic communities; improving tools for data-analysis through participating in developing or providing user feedback.

We do not want to merely investigate the datafied society, we also want to participate in building it!