Data colonialism through accumulation by dispossession: new metaphors for daily data

For our fourth RAD.GE meeting, we read through Jim Thatcher, David O’Sullivan and Dillon Mahmoudi’s (2016) paper ‘Data colonialism through accumulation by dispossession: New metaphors for daily data’, published in Environment & Planning D: Society and Space Vol. 34 (6): 990-1006.

This was a particularly fascinating discussion as were joined by colleagues from the Informatics forum here at the University of Edinburgh, who were far more clued up on issues of big data, privacy and technology than we were!

Everything We Do Is Now Big Data

The paper seeks to problematise those myriad ways in which we create ‘big data’ through mundane everyday activities (such as writing this blog, for example) and how this is then capitalised on by an increasingly data-hungry set of large corporations. The main line of argument is that individual patterns of web use, once bundled together into tradeable ‘big data’, has become a hugely profitable asset. In this way, our own time has been ‘colonised’ in what the authors see as an act of accumulation by dispossession.

Key points discussed

  • The paper is strangely passive. It totally overlooks the political debate around resisting this digital commodification, both at an institutional level (through legislation, or agitation within government) and at an everyday “anti-colonial” level, akin to James C. Scott’s “small” acts of resistance. We talked a lot about the question of agency within the paper, for the individual tech user (who may or may not engage in multiple techniques to hide their digital trail, however naively or competently) is narratively (and thus politically) absent.
  • One discussant responded to the question of resistance by suggesting we are in danger of fetishizing small/individual acts of resistance, when the processes at hand are so much bigger.
  • We found the question of consent very key to this argument, for if we are willingly handing over our data, than the violence implied by the accumulation by dispossession/colonialism metaphors is out of place. Whilst, on the one hand, a lack of knowledge means digital citizens are usually unable to resist, it is equally true that many people like or welcome the ‘ease’ provided by algorithmic knowledge of our patterns, and so on.
  • Data is, nowadays, often used in ways you wouldn’t expect. One colleague from Informatics put it rather neatly: “five years ago, if you geo-tagged yourself, they’d know where you were. Now if you geo-tag yourself, they know how likely it is that you’re depressed.” The fine-grain emotional detail provided by big data is at once technologically astonishing and emotionally terrifying. In particular we discussed the alleged role of data analysis in the Brexit and Trump victories.
  • Where does ‘the state’ end? If Amazon have all your data, do they become a form of ‘government’?
  • The way big data is framed in the paper (worthless in its component parts; traded as blocks) feels very similar to the bundling of sub-prime mortgages. Does big data actually have a value? Why or how does it generate such profit? Is this entirely fictitious and, if so, will the next financial crisis be caused by a data crash?
  • We discussed the metaphor of toxic waste to describe personal data in a digital world: dangerous to spill, very difficult to erase…
  • Is the “accumulation by dispossession” metaphor actually suitable when the data could not exist if we hadn’t purchased the devices to create it, and where the data (once taken) still exists in more than one place?
  • How does big data relate to older categorising technologies such as the census? Is this just statecraft through new technology? Does the sheer volume and minute detail of the data change what the data does (politically), or simply mean more of it?
  • The “left” (at least in a parliamentary sense) needs the state to know things in order to legislate around them. Is big data, like machines before them, wrongly vilified as the evil, when it is the owners of that data (and what they use it for) that is wrong?
  • What if the data is wrong? Is it still possible to act spontaneously or unexpectedly? How does this effect our sense of self?

 

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