Seeing and talking about Big Data, Farida Vis, AHRC Subject Assocations

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Seeing and talking about Big Data, Farida Vis, AHRC Subject Assocations

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Seeing and talking about Big Data, Farida Vis, AHRC Subject Assocations

  1. 1. Seeing and talking about Big Data Farida Vis, University of Sheffield @flygirltwo
  2. 2. ‘He created this installation that was at the Tate museum in London a while back and the installation was these hundreds of thousands of ceramic hand-painted sunflower seeds... And as you stood back from the room it looked like this sea of just stones that were black stones that were spread across the floor and of course you couldn’t really tell what they were. But as you got closer it looks like, you can start to tell ‘ooh it looks like they’ve stamped out hundreds of thousands of sunflower seeds and spread them across the floor’. But as you pick them up you started to realise that they were all individually shaped and painted differently and unique and beautiful and distinct in their own right. So that’s what we want to bring to what we’re building: the ability to shrink the world and allow everybody to see each other.’ Dick Costolo Twitter CEO, 2012 (quoted in Vis, 2012)
  3. 3. Synoptic view (Scott, 1998) a) Everything can be seen b) Everything can be comprehended
  4. 4. A critical reflection on Big Data: considering APIs*, researchers and tools as data makers *Application Programming Interfaces
  5. 5. Data companies, structures, algorithms
  6. 6. Data companies Structures Algorithms APIs Researchers Tools
  7. 7. Academic definition Big data includes cultural and technological aspects, but also highlights Big Data as a ‘scholarly phenomenon’, which rests on interplay between: • Technology: maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets. • Analysis: drawing on large data sets to identify patterns in order to make economic, social, technical, and legal claims. • Mythology: the widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy. (boyd and Crawford, 2012, p. 663).
  8. 8. Industry definition “Big data” is high-volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making’ (Gartner in Sicular, 2013). Part one: three Vs – high Volume, -Velocity, -Variety Key focus on processing data in real time. Part two: highlight cost-effectiveness and innovation in processing this data. Part three: key benefit is the possibility of greater insight and thus better decision-making
  9. 9. • Important to make visible inherent claims about objectivity • Problematic focus on quantitative methods • How can data answer questions it was not designed to answer? • How can the right questions be asked? • Inherent biases in large linked error prone datasets • Focus on text and numbers that can be mined algorithmically • Data fundamentalism
  10. 10. Crawford (2013): ‘“data fundamentalism,” the notion that correlation always indicates causation, and that massive data sets and predictive analytics always reflect objective truth’. Idea and belief in the existence of an objective ‘truth’, that something can be fully understood from a single perspective, again brings to light tensions about how the social world can be made known.
  11. 11. Barthes (1957) on myth: naturalize beliefs that are contingent, making them invisible, and therefore beyond question. Bowker and Star (2000): limitations of available ways in which information can be stored in society. Instead of seeing the limitations of the technical affordances and imagine different ways in which information might be structured, the ways in which information is structured become naturalized, people begin to see these structures as ‘inevitable’ (p. 108).
  12. 12. Data we want, but can’t have
  13. 13. Amazon awarded ‘Social Networking System’ patent (The United States Patent and Trademark Office, 15 June 2010) "A networked computer system provides various services for assisting users in locating, and establishing contact relationships with, other users. For example, in one embodiment, users can identify other users based on their affiliations with particular schools or other organizations. The system also provides a mechanism for a user to selectively establish contact relationships or connections with other users, and to grant permissions for such other users to view personal information of the user. The system may also include features for enabling users to identify contacts of their respective contacts. In addition, the system may automatically notify users of personal information updates made by their respective contacts."
  14. 14. Perfect recommendation storm?
  15. 15. Data we don’t know is collected
  16. 16. How can we talk about this? James Bridle (2013)
  17. 17. How to imagine future when present is hard to see?
  18. 18. Surveillance
  19. 19. More surveillance
  20. 20. Predictive policing
  21. 21. Seeing the matrix
  22. 22. Rise of the machines. No humans?
  23. 23. What does the internet look like?
  24. 24. Like this?
  25. 25. Or this?
  26. 26. Sadly more like this…
  27. 27. And this…
  28. 28. What does the cloud look like?
  29. 29. Facebook’s Swedish data centre
  30. 30. Inside…
  31. 31. Skynet
  32. 32. ‘The cloud is not an object but an experience and its particles are the very building blocks of a molecular aesthetic in which we live and act’ (CFP for The Transdisciplinary Imaging Conference)
  33. 33. How can we make algorithms visible?
  34. 34. What does the algorithm look like?
  35. 35. What/how does the algorithm see?
  36. 36. Is lady = wants baby
  37. 37. The human algorithm tension There are people in the machine 350 million images daily on FB
  38. 38. From around May 1996, just before Amazon’s IPO: ‘Soon, Amazon’s human editors were recommending books to customers based on similar purchases they had made in the past.’ ‘Amazon wasn’t just a selling site; it became an early social network site for book fans’. (Brandt, 2011, p. 86)
  39. 39. Trent Reznor: Chief creative officer at Daisy "What's missing is a system that adds a layer of intelligent curation . . . As great as it is to have all this information bombarding you, there's a real value in trusted filters. It's like having your own guy when you go into the record store, who knows what you like but can also point you down some paths you wouldn't necessarily have encountered. (from: http://www.rollingstone.com/music/news/trent-reznor-named-creative-chief-of-beats-daisy-music- service-20130110)
  40. 40. Finding new ways to see and talk about Big Data In particle physics, one of the bedrocks of Big Data in the natural sciences, so called ‘dark matter’ cannot directly be seen or observed by telescopes. Its presence can however be inferred by the gravitational effects it has on visible matter, specifically through the use of electromagnetic radiation. Drawing on particle physics, we can however adopt a similar approach and aim to make this unseen data and algorithmic structures visible by examining data that can be seen. Through such an examination we can infer and find out more about the dark matter’s gravitational effects on this visible data and learn more about the dark matter itself.
  41. 41. References • Roland Barthes, 1993 [1957]. Mythologies, London: Vintage Classics. • Brandt, R.L., (2011), One Click: Jeff Bezos and the rise of Amazon. London: Portfolio Penguin • James Bridle, 2013. ‘Naked Lunch’ Keynote presentation, Media Evolution Conference 2013 Malmo, Thursday 22nd August, http://bambuser.com/v/3836761 • Geoffrey C. Bowker and Susan Leigh Star, 2000. Sorting Things Out: Classification and its Consequences. Cambridge, Massachusetts and London, England: MIT Press. • danah boyd and Kate Crawford, 2012. “Critical Questions for Big Data,” Information, Communication & Society, volume 15, number 5, pp. 662-679. • Kate Crawford, 2013. “The Hidden Biases in Big Data”, Harvard Business Review, HBR Blog Network, 1 April, at http://blogs.hbr.org/2013/04/the-hidden-biases-in-big-data/, accessed 10 September 2013. • John C. Scott, 1998. Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed. New Haven and London: Yale University Press. • Svetlana Sicular, 2013. “Gartner's Big Data Definition Consists of Three Parts, Not to Be Confused with Three ‘V’s,” Forbes, 27 March, at http://www.forbes.com/sites/gartnergroup/2013/03/27/gartners-big-data-definitionconsists-of- three-parts-not-to-be-confused-with-three-vs/ , accessed 18 August 2013. • Farida Vis, 2012a. ‘‘’Twitter brings you closer’: the importance of seeing the little data in Big Data,” In: Drew Hemment and Charlie Gere (editors). FutureEverybody: FutureEverything Report, pp. 43- 45, at http://futureeverything.org/FutureEverybody.pdf, accessed 10 September 2013.

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