The Shadow of Big Data: Data-Citizenship and Exclusion

Big data are understood as being able to provide insights on human behaviour at an individual as well as at an aggregated societal level (Manyka et al. 2011).  These insights are expected to be more detailed and precise than anything before thanks to the large volume of digital data and to the unobstrusive nature of the data collection (Fishleigh 2014). Within this perspective, these two dimensions (volume and unobstrusiveness) define contemporary big data techniques as a socio-technical offering to society, a live representation of itself. More precisely, within a system theory approach (Luhmann 1982, 2010), large-scale big data work as a boundary-drawing operation where the difference between the system and its environment is reproduced. Here the result of system observation is defined by what is made visible as (big) data.

Within this process "data-citizenship" emerges. Data-citizenship assumes that citizens will be visible to the state through the data they produce. On a general level data-citizenship shifts citizenship from an intrinsic status of a group of people to a status achieved through action. This approach assumes equal possibilities of action for every citizen, even if research has shown that an unequal distribution of participatory potential is unavoidable (boyd & Crawford 2012).

Data-citizenships echoes what was envisioned by Luhmann (2010): When society is defined through the metacode of inclusion/exclusion, persons (of which citizens are equivalent within the legal system) are emancipated depending on their ability of being data producers.
To empirically explore this topic, the presentation will present preliminary results of an ongoing research about the digitalisation process in the Danish public sector.  Through the analysis of specific examples we will show how those citizens who do not leave (digital) traces not only appear at the margins or entirely disappear (Bourdieu 1999) in the shadow of big-data.