Should people negotiate financial income from the use of their personal data?
In their article “Free the data serfs?”1, The Economist quotes the argument that individuals should be allowed to collectively negotiate with platforms for financial income from the use of their data.
From the article:
“Glen Weyl, an economist at Microsoft, proposes”unions” that bargain on behalf of groups of people for a share of the income generated from the use of their data. The aim, says Mr Weyl, is not to destroy the platforms, just as labour unions do not want to shut down factories.” Along the same lines, “Andrew Yang, a former American presidential hopeful, has proposed a ‘digital dividend’ to individuals via collective bargaining.”
The proposition made by Glen Weyl, “unions that bargain on behalf of groups of people for a share of the income generated from the use of their data”, must be attractive to platforms. Mr Weyl’s suggestion would legitimate personal data collection deeds. His analogy is flawed though. That is because the nature of relations between factory owner and workers is different from the one between platforms and users. It goes without saying that users are not employees of platforms such as Facebook, Instagram, LinkedIn, or Google’s products. Still, users unintentionally provide platforms with input that feeds their business models (i.e. personal/behavioural data).2 The collection of behavioural data also enable the improvement of platforms to extend users’ “capacity for action by endowing them with cognitive resources”, a restitution in a sense. In this restitution stream, when algorithms feed data back into platforms to improve the experience of users, users are provided with incremental power in cognitive resources. This process of data collection and restitution confers platforms with a position of domination and simultaneously strips users from their autonomy because platforms pursue ends that are different from those of users’.3 The de facto domination of platforms over users and the oblivion of users would be prejudicial to negotiations.
As The Economist rightly suggests in their article, governments must weigh in. If individuals’ bargaining powers are too weak, as The Economist also suggests, public institutions should not only enact data protection rights, but also safeguards to protect individuals from selling personal data — as well as because personal data is intrinsic to one’s personhood.4
Like workers who have no choice to submit to the diktat of management because they can’t afford to give up their salaries to go on strike, users5 might have no choice but to surrender personal data for pecuniary crumbs as platforms herd them into serfdom.67
I send 3-4 emails per year with infos on how not to get screwed on the internet. You can take a read and sign up here.
You can also subscribe to the RSS feed of this website to receive new articles directly. If you have never used a RSS reader, read: how to escape Facebook’s filter bubble8 by following organizations and people you like directly via RSS.
The Economist, October 24th 2020 edition. The article is available on The Economist’s website under a different title: “Who owns the web’s data? The fightback against Big Tech’s feudal lords has begun”↩︎
Soshana Zuboff, The Age of Surveillance Capitalism, Current affairs, 2019↩︎
Cedric Durand, Techno-Féodalisme, La Découverte, 2020 p.126-127↩︎
article coming soon. Subscribe to the RSS feed of this website to receive new articles directly. If you have never used a RSS reader, read: how to ditch Facebook by following organizations and people you like with an RSS reader.↩︎
the term “user” was borrowed from narcotics by computer pundits in the 70s’↩︎
The Economist, Data workers of the world, unite, July 7th 2018 edition,↩︎
Cedric Durand, Techno-Féodalisme, La Découverte, 2020↩︎
A filter bubble is a state of intellectual isolation that allegedly can result from personalized searches when a website algorithm selectively guesses what information a user would like to see based on information about the user, such as location, past click-behavior and search history.↩︎