LAUSR

We ran a sociological software project (ESRC ES/KO10786) between 2013 and 16 on “A Sociology of Values and Value” (Beverley Skeggs and Simon Yuill 2016). It began as a study of how the affects of friendship and faith were monetized, but it quickly—because this is what our data identified—turned into a study of the tracking and trading of people’s data. There was so much tracking on people’s browsers that at first, we were overwhelmed. Then, we struggled to make sense of the radically different amounts of tracking and trading that occurred between people. One case stands out. It was the difference we saw between two women: one, a middle-class highly educated global journalist (Lara) and another an older, disabled women in receipt of social security (Belle). Our data showed how the trackers and traders were ever-present on Lara’s browsers, offering her browser use up for sale on digital advertising auction sites thousands of times per day. She was very valuable trade. Yet they were barely interested in Belle. At first, we thought we had a software design fault, or server problem with capturing data, but then we looked more closely. Belle was being targeted and traded, but only by debt agencies, and occasionally Asda. Belle was traded for her lack of buying power, her lack of economic value. Who was responsible for this digital divide? Clearly, the value identifying algorithms built for the purpose of tracking the trading potential of people for advertisers. From primitive accumulation to planned economies to computational capitalism, the key driver in the formation of any algorithm is usually the search for value. We have seen this take different shapes, such as the story of the drive for computational power in the formation of the Soviet Union’s food distribution system but mainly we see algorithms used and/or appropriated to shore up the interests of the rich and the powerful as they seek to constantly accumulate. Yet whilst algorithms underpin a huge amount of our daily infrastructure, such as assessing our value for trading purposes every time we open a browser, they do so by stealth. We do notice some, especially when they carry the intersectional inheritances of their designers, and when we see adverts sent to us as a result of our browsing history. Yet we rarely pay attention to the way algorithms turn us into trade as our data persona points are deand re-aggregated, put up for auction, sold, remaindered, and reaggregated. Trading one’s information happens in less than a millisecond (less than the time it takes to blink). There may be 100,000 bids for your data per second (especially if you are Lara). That means trading data occur faster than the speed of light.