Theorising the relationship between data and agency

My notes on Kennedy, H., Poell, T., & van Dijck, J. (2015). Data and agency. Big Data & Societyhttps://doi.org/10.1177/2053951715621569

This short paper is the editorial for a special section of big data & society, exploring the role of agency in relation to data. Their starting point is the tendency in the literature to focus on how datafication – which they define as rendering as data what was not previously quantified – can be used for surveillance and control, though itself an expression of the agency of elites. What gets lost are the “small-scale public organisations, community groups and activists are experimenting with the possibilities of datafication, pursuing objectives which are distinct from those of big brother’s uses of Big Data” (1). Data captures the situation of users but it is also often fed back to users, allowing them to orientate themselves in the world. It is for this reason that they delve into the structure/agency debate, in search of theoretical resources to make sense of the relationship between data and agency. They see the tension between control and resistance as an iteration of the structure/agency question. This is how they summarise the papers in the issue. From pg 3:

The five contributions aim to consider the extent of the dominance of the structures of datafication, the possibility of agency, and the spaces in between. At the same time, the contributions seek to combine critical perspectives on datafication with the perspectives of actors within data mining practices. The aim is to enrich our understanding of data and datafication, by bringing together structural analyses with recognition of individual agency in the context of these structures.

Interestingly, they draw a parallel between the relative denial of reflexivity you find in a thinker like Bourdieu with digital theories which stress the role of infrastructure and computation. But I thought this section was otherwise quite weak, drawing a contrast on the basis that “acting with agency is not necessarily reflexive or moral; it is not necessarily good” (3) for Bourdieu, whereas it seems pretty evident to me that these debates were about the character of social reproduction not about the cultural politics of reflexivity.

I like the brief discussion of the ontological implications of the term datafication, in  relation to the paper by Pybus et al. This helped me articulate why I’ve always found the term datafication unhelpful: by foreground the outcome and nominalising the process, it obscures the agency of those who are doing the datafying and those who are on the receiving end of it. It ends on pg 6 with a very helpful overview of key questions concerning how citizens can be empowered in relation to data:

To participate in datafied social, political, cultural and civic life, ordinary people need to under- stand what happens to their data, the consequences of data analysis, and the ways in which data-driven oper- ations affect us all. Can ordinary people do the same things with their data as corporations and organisa- tions? Citizens tend to give away data to platforms that facilitate their daily communication, automatically donating personal data about their health and fitness to apps like Runkeeper or Strava or sharing experiences of illness on healthcare tools like CureTogether. But what do they know about the data streams they help to gen- erate? How conscious are users of the ways in which their data are technically steered, repurposed, and resold? Platform sites cultivate certain styles of use, but actual users help steer how data become valuable. The distinction between the ‘technological unconscious’ (a term used by Beer, 2009) and the ‘conscious user’ becomes relevant here: how much do users know about data streams and their abilities to control them? The Quantified Self movement is one example of individuals attempting to take ownership of their own data (for example Nafus and Sherman, 2014), although critics point out that corporations ultimately benefit from these data-gathering practices (Crawford et al., 2015).

This is coupled with another interesting observation that digital workers designing these systems are often assumed to be responsible, as if they were all powerful.

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