The Digital Culture and Communication section of ECREA
Dr Tanya Kant is a Lecturer in Media and Cultural Studies at the University of Sussex, UK. Her research interests include algorithmically personalised media, algorithmic governance in everyday life, computational culture and the constitution of (digital) identity. Email: firstname.lastname@example.org
Increasingly, critical approaches to algorithmic profiling have theorised the ways in which individuals are not just reflected in but constituted by those profiles – as Cheney-Lippold states, ‘we are data’ (2017). This paper takes forward claims that the ‘algorithmic self’ plays a powerful role in how identity is performatively constituted, yet seeks to question theoretical approaches that treat the datafied self as a unifying, equalising or potentially liberating subject position. Instead the paper argues that the though the ‘algorithmic self’ might seem to hold some political potential as a ‘post-identity’ configuration, the ‘retranslation’ of the algorithmic self back to recognisable identity markers (Bolin and Andersson-Schwarz, 2015) – by both commercial enterprise and users themselves – mean that the self ‘inside’ the algorithm can only be understood by stepping ‘outside’ of computational categorization.
Cheney-Lippold (2017) compellingly argues that because of the modulatory, self-referential process that constitutes identities in the database, the ‘measurable types’ that come to define users is open to a form of ‘post-identity politics’. In other words, ‘measurable-type identity is beholden to algorithmic fit, not the disciplinary confines of political identity’ (2017: 66). Thus, to be algorithmically identified as ‘male’ when one is cultural identified as ‘female’ reconstitutes not just users’ selfhood but can perhaps productively redefine normative categories of identity in themselves.
Building on this notion, the paper explores the everyday cultural politics of potentially being ‘beholden to algorithmic fit’ rather than political identity. Focusing on the algorithmic anticipation of users’ gender by commercial web platforms, the paper explores web user responses to being algorithmically identified as ‘female’. Though still a work-in-progress, this research looks to analyse audience responses to gender-targeted ads on social media – such as targeted ads for ClearBlue Ovulation tests (2013, 2013a) – to examine the ways in which users might be ‘seeing themselves through the eyes of the algorithm’ (Bucher, 2016: 66). I consider the implications of being algorithmically identified as female, exploring both the ideologically repressive interpellations of being made as a ‘fertile female’ in data, and the extent to which being made in data might challenge or reinforce being made as a ‘fertile female’ outside of the algorithm.
By considering the self as constituted both in and outside the algorithm, the role of algorithms as ‘cultural machines’ (Finn, 2017) becomes apparent: both in audience responses, and perhaps in Cheney-Lippold’s statement that algorithmic categorizations might escape the disciplinary regimes of established ideas of identification. By this I mean that the socio-cultural relations that go into making the ‘idea’ of algorithms might help us to understand the tense relation between ‘computation and material reality’ (Finn, 2017: 10) that ultimately ‘makes’ users.
Turning to theorisations of digital ‘women’s work’, I propose that confronting, negotiating and making sense of one’s own datafied self constitutes a form of immaterial labour (Jarret, 2015). I argue that consuming female-specific ads might be considered (double) digital women’s work – wherein the experience of being algorithmically profiled as ‘female’ both commodifies user interactions and imposes an unequal burden of socio-technical classification. Though we might be data, the ways in which users must make sense of their algorithmic constitution is enacted differently – and potentially unequally – in different web users’ everyday engagements. Critical approaches to big data and algorithmic governance must therefore take into account life outside the algorithm if we are to understand the nuances, complexities and structural inequalities inherent in making sense of datafied selves.
Finally I explore the implications that user attention might have on algorithmic anticipation, drawing on Bucher’s (2016) claim that the performative work of algorithms takes place not only ‘in’ the algorithm, but in users’ negotiations with the algorithm. By examining the idea that ‘We are data’ from the perspective of lived experience, the paper looks to highlight how critical approaches to algorithmic identity could more intricately address the gender inequalities inherent in algorithmic profiling – especially the digital women’s work that comes with making sense of oneself in and outside the algorithm.
Keywords: datafied selves, gender inequalities, algorithmic profiling, everyday life
Bolin, G. & Andersson-Schwarz, J. (2015) ‘Heuristics of the algorithm: Big Data, user interpretation and institutional translation’, Big Data and Society. 2. doi:10.1177/2053951715608406.
Bucher, T (2016) ‘The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms’, Information, Communication & Society, 20:1, 30-44, DOI: 10.1080/1369118X.2016.1154086.
Clearblue (2013) ‘A Better Chance of Getting Pregnant with Clearblue Advanced Ovulation Test’, YouTube, https://www.youtube.com/watch?v=F1gXFyQtgCs (Accessed April 2017).
Clearblue (2013a) ‘Clearblue advanced pregnancy test commercial’, Youtube https://www.youtube.com/watch?v=p9lPaaYxsVQ , (Accessed April 2017).
Finn, Ed (2017) What Algorithms Want. Massachusetts: MIT Press.
Jarrett, K. (2015) Feminism, Labour and Digital Media: The Digital Housewife, London: Routledge.