We live in a society in which techniques of algorithmic calculation are routinely shaping the ways in which we pass through a variety of on- and offline spaces. But what methods can be used to render more visible these usually unseen practices? Are there ways of ‘hacking’ or reverse engineering this algorithmic work? This paper presents some exploratory research in this area, focusing the calculative practices surrounding a new type of consumer credit lending. In making their lending decisions, consumer credit lenders have historically relied on data about a borrower’s track record with credit products, often supplied by third party credit reference agencies. However, in world of subprime, short term lending – so called payday lending – a new model is emerging, which relies on the lender itself gathering information about its current and future user base by extracting, compiling, and algorithmically processing a highly diverse range of often unwittingly leaked online ‘traces’ from potential borrowers. In the UK the most high profile of these is the controversial payday lender Wonga, although it has major, well-funded overseas rivals which are launching new operations in a wide range of countries. All rely, to varying degrees, on combining data obtained from third parties with information about a user’s online behaviour harvested using a variety of online tracking tools. This paper presents methods for tracking these tracking tools, as a way of beginning to unearth the specific calculative practices they imply. At the same time, it points towards the limits of such methods, arguing that in some cases researchers may be forced to rely on forms of ‘speculative empiricism’. 


This seminar is part of the 'Methods Mixtures' series presented by the Centre for Science Studies and Centre for Gender and Women's Studies. For further information on this seminar series please contact Maggie MortThe full list of seminars this term can be downloaded below:

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