The Feel of Algorithms by Minna Ruckenstein;

Author:Minna Ruckenstein;
Language: eng
Format: epub
ISBN: 9780520394568
Publisher: University of California Press

Obscurity as a Market Logic

Once the fear and paranoia have started to crystallize, contradictory evidence will no longer change the state of affairs. The fears related to data extraction and algorithms might initially be bred by lack of accurate information, with people forming their opinions based on skewed or missing facts, yet the distrust also derives from the ensnaring nature of digital services; as people start to use such services, they voluntarily, if unwittingly, become enmeshed in the webs of information that fuel service operations. For example, around 2015 Finns began ordering 23andMe genetic test kits, which gave them access to personalized ancestry maps and charts listing elevated health risks. This was done with little understanding of the less public agenda of the company, which was to get consumers involved in the production of a database that can be used for building corporate partnerships (Ruckenstein, 2017). The way the market logic of direct-to-consumer genetic testing is hidden, including the commercialization of health data and participatory research initiatives, speaks of a more general characteristic of companies, that of concealing their value extraction mechanisms. The rhetoric of openness and participation widely promoted by digital platforms obscures the lack of transparency regarding economic pursuits related to uses of personal information.

Ultimately, it is difficult to acquire firsthand information about value extraction mechanisms other than from company-initiated blogs or news sites. When people recognize the extractive uses of their data and how their participation is integrated into the business logic, which sometimes happens long after they have started to use a service, they can feel betrayed. For example, once people realize that the genetic data uploaded to the 23andMe service can be sold to pharmaceutical companies, with no financial dividend for the test takers whose data traces make up the database, they start to look more carefully at how the service actually operates. Ironically, with their test purchases they had paid a fee to become contributors to resalable health data sets. In light of the oppositional structure of feeling, it is thus notable that breakdowns in trust direct attention toward “unpicking how the system works” (Bishop, 2019, p. 2592). Only after the trust is lost do people begin to pay attention to what actually happens with the aid of data they have supplied.

At times, feelings of being deceived are directly related to the privacy settings of services. Sara recounts how she participated in a group discussion on Telegram, an instant messaging app that is advertised as a more privacy-friendly service than WhatsApp. After exchanging numerous messages on the assumption that they were private, the group of friends suddenly realized that group Telegram discussions are searchable by anybody. It turned out that in order to have a private conversation, one needs to switch on the appropriate settings. Sara describes feeling shaken, because she and her friends had written openly about their private lives. What this example brings to the fore is that privacy is not a default setting, but something that people need to guard and protect on their own initiative.

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