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  Mark 8:01 pm on November 11, 2019 Permalink | Reply
  Tags: anti-hermeneutic, data science, evisceration, hermeneutics ( 2 ), human agency, machine learning ( 8 ), platform epistemics   

  Humans as blackboxes, machines as transparent 

  From Automating Inequality by Virginia Eubanks pg 167:

  Parents in Allegheny County helped me articulate an inchoate idea that had been echoing in my head since I started my research. In Indiana, Los Angeles, and Allegheny County, technologists and administrators explained to me that new high-tech tools in public services increase transparency and decrease discrimination. They claimed that there is no way to know what is going on in the head of a welfare caseworker, a homeless service provider, or an intake call screener without using big data to identify patterns in their decision-making. I find the philosophy that sees human beings as unknowable black boxes and machines as transparent deeply troubling. It seems to me a worldview that surrenders any attempt at empathy and forecloses the possibility of ethical development. The presumption that human decision-making is opaque and inaccessible is an admission that we have abandoned a social commitment to try to understand each other. Poor and working-class people in Allegheny County want and deserve more: a recognition of their humanity, an understanding of their context, and the potential for connection and community.

   

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  Mark 8:27 am on May 20, 2019 Permalink | Reply
  Tags: acoustics, closed social science, commercial social science ( 2 ), corporate social science, data science, echo chambers, Helen margetts, Social Media ( 109 )   

  The acoustics of social media 

  Margetts, H. (2017). Political behaviour and the acoustics of social media. Nature Human Behaviour, 1(4), s41562-017.

  I’ve tended towards acoustic metaphors when making sense of social media since finding inspiration in a remark by David Beer about the challenge of being heard above the din on social media. These platforms are cacophonous, as many voices struggle for attention in forums which have been structured hierarchically around visibility and popularity. So this paper’s focus on acoustics immediately drew my attention. Margetts notes that “around 128 million people across the United States generated 8.8 billion likes, posts, comments and shares related to the election on Facebook alone” and “US Twitter users can even witness the development of policy as it is formed or contested, given the new president’s enthusiasm for this media platform”. Not only is there much material than ever to encounter, much of it is social information about what other people are thinking and doing. Much of this has obvious political ramifications, with the internal architecture of platforms shaping the flow of social information around networks:

  On any screen we can see in real time how many other people, or how many of our friends, have liked, shared, viewed or searched for an item, and can often gauge which items are becoming popular (‘trending’) or how close a campaign or fund-raising initiative is to meeting a target.

  This social information includes ‘signals of viability’ in which incipient mobilisations gather steam (or fail to) based on how widespread their potential support is seen to be. Claims about echo chambers and filter bubbles are fundamentally theories about social information circulating in closed circuits, such that “inhabitants of these ideologically narrow environments are vulnerable to distorted versions of events or fake news, which bounce around the chamber and become regarded as the truth”. In this sense, argues Margetts, the acoustics of social media are held to be responsible for political populism, as it is claimed that echo chamber effects drive polarisation by dragging people to ever more extreme positions on either side of a dichotomy.

  However these claims need to be carefully considered. The fact that “our digital networks are so much larger and more heterogeneous than anything we’ve had before” means that one could easily argue the real echo chamber would be someone who reads the same daily newspaper, without exposure to any other sources of social information. Are digital echo chambers just reproducing those which already existed offline, in a way driven by homophily effects which predate social media? A range of studies have suggested the heterogeneity of online networks means that users receive news from more sources than non-users, with deliberate choice playing more of a role than algorithms in shutting down information flow.

  As Margetts puts it, this “highlights the wide spectrum between a sealed echo chamber and random news content, just as similar buildings can vary widely in their acoustic effects”. There is great potential in experimental methods for investigating these issues, with every platform other than Twitter being closed to researchers unless they can secure the explicit cooperation of the firm. This has contributed to a situation in which “Speculation over the existence of echo chambers, and their interaction with other pathologies of social media such as fake news, vastly outpaces any experimental studies of their existence”.

  This is a huge problem because many of the pressing issues of our time would require access to such data. The link between social science and social problems risks being broken by the opacity of corporate platforms. Margetts ends with by reflection on the “need for institutional catch-up” when social media is barely a decade old.

   
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  Mark 8:32 am on February 5, 2019 Permalink | Reply
  Tags: data science, social data science   

  The Data Science & Society Institute 

  This looks like an interesting job at a new institute I’d like to keep track of:

  The Department of Science & Technology Studies at Cornell University seeks
  a Postdoctoral Researcher to play a major role in a two-year project on
  Data Science & Society. We invite applications from scholars with a recent
  Ph.D. in science & technology studies (STS) or related fields (e.g.,
  sociology, anthropology, law, media studies, information science) and an
  active research agenda on the social aspects of data science.

  The Postdoctoral Researcher will be expected to devote 50% time to his or
  her own research agenda and 50% time to working with S&TS Department
  faculty on developing the Data Science & Society Lab, a new and innovative
  course that is part of the Cornell Data Science Curriculum Initiative. The
  lab will engage undergraduate students in two components: instruction in
  theoretical tools and practical skills for analyzing social and ethical
  problems in contemporary data science (e.g., data science as ethical
  practice; fairness, justice, discrimination; privacy; openness, ownership,
  and control; or credibility of data science); and participation in
  interdisciplinary project teams that work with external partners to address
  a real-world data science & society problem.

  The Postdoctoral Researcher will have the opportunity to help launch and
  shape the initiative, to develop curriculum and engagement projects, build
  relationships with external partners and participate in teaching the
  course. S/he will work with two S&TS Department faculty members, Malte
  Ziewitz and Stephen Hilgartner, who will have primary responsibility for
  teaching the course.

  Applicants should send:

  • Cover letter summarizing the candidate’s relevant background,

  accomplishments, and fit with the position

  • CV
  • Up to two publications (or writing samples)
  • Three letters of recommendation
  • A transcript of graduate work (unofficial is acceptable)

  Required Qualifications:

  PhD in science & technology studies (STS) or related fields (e.g.,
  sociology, anthropology, law, media studies, information science) and an
  active research agenda on the social aspects of data science. ABD students
  are eligible to apply, but proof of completion of the Ph.D. degree must be
  obtained prior to beginning the position. Recent graduates who received
  their Ph.D. during the last five years are especially encouraged to apply.

  The position is available for a Summer 2019 start (as early as July 1). We
  will begin to review applications on February 28. Apply at
  https://academicjobsonline.org/ajo/jobs/13236. For further information,
  please contact Sarah Albrecht, saa9@cornell.edu.

  Diversity and inclusion are a part of Cornell University’s heritage. We are
  a recognized employer and educator valuing AA/EEO, Protected Veterans, and
  Individuals with Disabilities.

   
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  Mark 9:58 am on January 25, 2019 Permalink | Reply
  Tags: big data ( 49 ), data science, social listening ( 3 )   

  The Politics of Big Data: Social Listening and Reclaiming the Future 

  From an interesting British Academy funded workshop last November:
  https://methods.sagepub.com/video/embed/srmpromo/oohinf/social-listening-and-reclaiming-the-future

   
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  Mark 7:04 am on October 21, 2018 Permalink | Reply
  Tags: data science, Digital Capitalism ( 33 ), digital elites ( 21 ), digital social theory ( 18 ), silicon valley ( 12 ), tech leaders   

  The organic sociology of Silicon Valley 

  I find it hard not to wonder how many interactions like this one, satirised so superbly by Silicon Valley, lie behind the tendency of digital elites to pontificate about social trends. On the one hand, data science emerges as a powerful means of generating knowledge of social life, building on past developments while becoming something distinct. On the other hand, a cultural climate which inflates and incentivises self-styled visionaries and thought leaders can imbue vacuous musings with epistemic authority. The organic sociology of Silicon Valley is a strange and complex beast.

   
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  Mark 7:19 am on October 13, 2018 Permalink | Reply
  Tags: Archive ( 1,263 ), data science, data scientists ( 3 ), Digital Social Science ( 56 ), Thinking ( 628 )   

  Where do data scientists come from? 

  A few years ago, I was preoccupied by this question after an illuminating six months working in the Data Science Lab at Warwick Business School. I co-organised a ground breaking conference in computational social science and it was clear this represented a mode of expertise distinct from what I had previously been familiar with as someone whose academic life had been spent until then in philosophy and sociology departments. It made different claims and responded to different concerns, leading to analyses which were radically extensive but lacking in intensivity. However what interested me more were the human factors associated with it, the distinctive stylings and presentations I could see amongst people who identified as data scientists. It struck me as a fascinating category, doing a great deal of work while remaining under defined. It could be performed in a wide range of ways, while there remained characteristics which most data scientists seemed to share. It placed intellectual issues in the foreground while there nonetheless seemed to be an emerging ethos uniting people who identified as data scientists, even if it might only have been partially articulated.

  What did it mean to be a data scientist? How did the identity motivate people? How did these feed back into the occupational category and the role this played within data driven organisations? These are questions which haven’t been on my mind for a while but I was reminded of them today when reading about DJ Patil, the first chief data scientist in the US government. This was the account he gave to Michael Lewis on pg 155 of the Fifth Risk, describing how the category of ‘data scientist’ emerged amidst the data tsunami of the last decade. A range of titles and roles had emerged which packaged up emerging competencies related to data in different ways. According to DJ Patil the impulse to unify these under the master category of ‘data scientist’ was bureaucratic, reflecting a managerial discomfort with an explosion of new categories within their organisations:

  Along with much more: in the space of a few years, the interest in data analysis went from curiosity to fad. The fetish for data overran everything from political campaigns to the management of baseball teams. Inside LinkedIn, DJ presided over an explosion of job titles that described similar tasks: analyst, business analyst, data analyst, research sci. The people in human resources complained to him that the company had too many data-related job titles. The company was about to go public, and they wanted to clean up the organization chart. To that end DJ sat down with his counterpart at Facebook, who was dealing with the same problem. What could they call all these data people? “Data scientist,” his Facebook friend suggested. “We weren’t trying to create a new field or anything, just trying to get HR off our backs,” said DJ. He replaced the job titles for some openings with “data scientist.” To his surprise, the number of applicants for the jobs skyrocketed. “Data scientists” were what people wanted to be. In the fall of 2014 someone from the White House called him. Obama was coming to San Francisco and wanted to meet with him.

  Exploring the interplay between organisations creating roles for ‘data scientists’ and individuals coming to identify as such would make a fascinating project. How have the role of the “analyst, business analyst, data analyst, research sci” and the people performing them changed in the process? For instance consider the description on Pg 172-174 of the intellectual project which the creation of chief data scientist in the US government sought to advance:

  DJ Patil had gone to Washington in 2014 to help people find that gold. He was the human expression of an executive order Obama had signed the year before, insisting that all unclassified government data be made publicly available and that it be machine-readable. DJ assumed he’d need to leave when the man who hired him left office, so that gave him just two years. “We did not have time to collect new data,” he said. “We were just trying to open up what we had.”

  How do these organisational projects shape how the data scientists involved see their purpose? How do these projects contribute to a sense of data science as a vocation beyond the organisation in question? How should we revise our conception of organisations if all those beyond a certain size will have data scientists within their ranks? There are many questions which can be asked here and investigating the role itself would be an interesting route into them.

   
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  Mark 6:46 pm on February 6, 2018 Permalink | Reply
  Tags: Archive ( 1,263 ), Archiving ( 115 ), big data ( 49 ), computational social science ( 9 ), data science, Digital Capitalism and Digital Social Science ( 128 ), Digital Social Science ( 56 ), Digital Sociology ( 115 ), social ontology ( 43 ), The Political Economy of Digital Capitalism ( 224 )   

  Social listening and reclaiming the future 

   
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  Mark 10:46 am on June 29, 2017 Permalink | Reply
  Tags: alexander nix, Archive ( 1,263 ), cambridge analytica ( 2 ), clinton campaign, data science, Digital Elections, Party Politics and Diplomacy ( 64 ), Digital Inequalities ( 40 ), electoral data science, Post-Democracy, Depoliticisation and Technocracy ( 109 ), Shadow Mobilization, Astroturfing and Manipulation ( 52 ), The Political Economy of Digital Capitalism ( 224 ), Thinking ( 628 ), Uncategorized ( 1,962 )   

  The data warriors and the electoral wars they wage 

  One of the most interesting issues raised by the rise of data science in party politics is how to untangle corporate rhetoric from social reality. I have much time for the argument that we risk taking the claims of a company like Cambridge Analytica too seriously, accepting at face value what are simply marketing exercises. But the parallel risk is that we fail to take them seriously enough, dismissing important changes in how elections are fought as marketing hype propounded by digital charlatans.

  Perhaps we need to focus more on the data scientists themselves. As much as there is something of the Bond villain about Alexander Nix, CEO of Cambridge Analytica, it’s important that we don’t become preoccupied with corporate leaders. Who are the rank-and-file data scientists working on campaigns? What motivates them? How do they conceive of the work they do? There were interesting hints about this in the recent book Shattered, looking at Hilary Clinton’s failed election campaign. Much as was the case with Jeb Bush’s near entirely stalled campaign, there had been much investment in data analytics, with buy-in right from the top of the campaign. From pg 228-229:

  These young data warriors, most of whom had grown up in politics during the Obama era, behaved as though the Democratic Party had come up with an inviolable formula for winning presidential elections. It started with the “blue wall”—eighteen states, plus the District of Columbia, that had voted for the Democratic presidential nominee in every election since 1992. They accounted for 242 of the 270 electoral votes needed to win the presidency. From there, you expanded the playing field of battleground states to provide as many “paths” as possible to get the remaining 28 electoral votes. Adding to their perceived advantage, Democrats believed they’d demonstrated in Obama’s two elections that they were much more sophisticated in bringing data to bear to get their voters to the polls. For all the talk of models and algorithms, the basic thrust of campaign analytics was pretty straightforward when it came to figuring out how to move voters to the polls. The data team would collect as much information as possible about potential voters, including age, race, ethnicity, voting history, and magazine subscriptions, among other things. Each person was given a score, ranging from zero to one hundred, in each of three categories: probability of voting, probability of voting for Hillary, and probability, if they were undecided, that they could be persuaded to vote for her. These scores determined which voters got contacted by the campaign and in which manner—a television spot, an ad on their favorite website, a knock on their door, or a piece of direct mail. “It’s a grayscale,” said a campaign aide familiar with the operation. “You start with the people who are the best targets and go down until you run out of resources.”

  Understanding these ‘data warriors’ and the data practices they engage in is crucial to understanding how data science  is changing party politics. Perhaps it’s even more important than understanding high profile consultancies and the presentations of their corporate leaders.

   
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  Mark 11:00 am on January 24, 2017 Permalink | Reply
  Tags: Archive ( 1,263 ), Becoming Who We Are ( 54 ), behaviourism ( 5 ), data science, Digital Capitalism and Digital Social Science ( 128 ), Digital Sociology ( 115 ), humanism ( 5 ), social theory ( 108 ), Thinking ( 628 )   

  Devushkin as the Eviscerated Human 

  I just came across the following passage in this paper by Anna Mary Cooper and Jenna Condie:

  Bakhtin’s  (1984a)  literary  analysis  of  Dostoevsky’s  novel ‘Poor Folk’ shows how the character Devushkin, who in recognising himself in another  story, did not wish to be represented as ‘something  totally  quantified,  measured, and defined to the last detail: all of you is here, there is nothing more in  you,  and  nothing  more  to  be  said  about  you’ (p.  58).

  http://socialtheoryapplied.com/journal/jast/article/view/31/7

  This is exactly what I’m trying to get at with the notion of eviscerating the human: analysing human beings in ways which empty out their thoughts, feelings, dreams and aspirations in order to leave only the transparently measurable aspects that lie beneath their recalcitrant minds. What Mark Andrejevic describes in InfoGlut as corporeal literacy: “the attempt to bypass the vagaries of speech in order to get directly at the true underlying emotions that speakers all too often attempt to mask” (pg. 81). This involves constructing what I’ve come to think of as evisceration devices: tools and techniques, in reality dependent upon conceptual proxies, enabling what is ‘inner’ to either be dispensed with or reduced to a corporeal manifestation of it.

  If we can identify this as an intellectual project which wins the commitment of many powerful individuals and groups, we can begin to ask sociological questions about their interests and investments in it. The slightly grandiose and perhaps vague terminology of ‘eviscerating the human’ serves a methodological purpose because this is a project that cuts across multiple social domains and does not involve an overlapping awareness of being involved in a shared project. It is attempt to conceptualise the shared characteristics of something that can be seen across a multiplicity of activities, rather than a single endeavour undertaken by a collective with a shared commitment.

   
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  Mark 10:53 am on January 19, 2017 Permalink | Reply
  Tags: Archive ( 1,263 ), behaviourism ( 5 ), data science, Digital Capitalism and Digital Social Science ( 128 ), Digital Inequalities ( 40 ), evisceration of the human ( 2 ), hermeneutics ( 2 ), human ( 3 ), interpretation ( 3 ), Philosophy of Technology ( 105 ), the human ( 2 ), The Political Economy of Digital Capitalism ( 224 ), Thinking ( 628 )   

  The Evisceration of the Human 

  In a paper I’m writing for the first volume of the next Centre for Social Ontology project, I’m offering an analysis of what I call the evisceration of the human. I understand this as an intellectual project which seeks to get beyond self-understanding, hollowing out the phenomenological froth which characterises the interpretative human and getting to the underlying behavioural reality beneath it. It’s a project which, as Mark Andrejevic puts it in Infoglut, seeks to “sidestep self-understanding and self-representation to get at these recalcitrant minds directly” (p. 86). Interiority is reduced to empirical proxies, proliferating ad hoc hypothesis which explain away the apparent reality of the first-person perspective and reduce it to measurable and testable behavioural factors.

  This operational abstraction constitutes a kind of ‘hollowing out’ of the human, seeking to reduce the category to its underlying behavioural reality rather than trying to cope with it in its bewildering wholeness. The instruments change, the precise formulation of the ad hocery changes but the underlying direction of explanatory travel remains the same. That at least is my hunch, though there’s a huge amount of work in the history of ideas which I’d have to undertake to justify it properly.

  My focus however is on the present enthusiasm for eviscerating the human we see associated with digital technology and digital data. I really like this formulation from Audrey Watters, on loc 1245 of her The Monsters of Educational Technology:

  We are now creating data at an unprecedented scale, with unprecedented velocity and increasing complexity. The temptation is to believe that if we can just collect all the data from our students – all their clicks –run it through an algorithm, do a little pattern-matching, and we’ll solve everything, we’ll unlock the secrets of the human brain, we’ll unlock the potential of each child.

  The allure of our new instruments plays a crucial role in this iteration of the evisceration project. We believe that if only we were to collect enough data, the inner truth would be revealed. The secrets are there, waiting to be revealed, if only we can mine down into the human with enough precision and accuracy.

   
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  Mark 8:40 am on January 13, 2017 Permalink | Reply
  Tags: Archive ( 1,263 ), Corporate Culture, Elites and Their Self-Understandings ( 90 ), data ( 20 ), data science, Digital Capitalism and Digital Social Science ( 128 ), Digital Social Science ( 56 ), ideology of data, ontology of data ( 3 ), philosophy of technology ( 26 ), The Political Economy of Digital Capitalism ( 224 ), The Technological History of Digital Capitalism ( 137 ), Thinking ( 628 )   

  The ontology of data, the ideology of data  

  In a post yesterday, I expressed my discomfort with how Nick Srnicek invokes the notion of data as a raw material in his Platform Capitalism. In a footnote on  loc 1102-1121, he offers a Marxist justification for this use:

  I draw here upon Marx’s definition of raw material: ‘The land (and this, economically speaking, includes water) in the virgin state in which it supplies man with necessaries or the means of subsistence ready to hand, exists independently of him, and is the universal subject of human labour. All those things which labour merely separates from immediate connexion with their environment, are subjects of labour spontaneously provided by Nature. Such are fish which we catch and take from their element, water, timber which we fell in the virgin forest, and ores which we extract from their veins. If, on the other hand, the subject of labour has, so to say, been filtered through previous labour, we call it raw material; such is ore already extracted and ready for washing’ (Marx, 1990: 284–5, emphasis added).

  The distinction between natural and processed helps address the charge that data-as-raw-material naturalises data but it doesn’t avoid it all together. It recognises that usable data is always processed and cleaned prior to use, ‘filtered through previous labour’ both directly (wrangling etc) and indirectly (socio-technical systems of generation, capture, storage and analysis).

  But does it obscure the character of its applications? The original sources of this ‘raw material’ are intensely social, unlike that extracted from nature, arising from the digitalisation of action and transaction within a market economy. The applications of this ‘raw material’ are intensely variable, with data sometimes being a direct source of value that can be realised through a linear activity, other times being an informational good that can be used to coordinate and calibrate and perhaps more often being a source of confusion and speculation.

  It would take constructionism too far to deny the ontology of data. Indeed, I think a concern for the ontology of data is a crucial vector through which we can understand the ideology of data. But we nonetheless need to carefully  unpick the way ‘data’ is represented by social actors, the interests served by this and the discursive architecture through which these interests are advanced in different organisational settings.

  There’s a fascinating example of this ideology which Srnicek quotes on loc 1296:

  If collecting and analysing this raw material is the primary revenue source for these companies and gives them competitive advantages, there is an imperative to collect more and more. As one report notes, echoing colonialist ventures: ‘From a data-production perspective, activities are like lands waiting to be discovered. Whoever gets there first and holds them gets their resources –in this case, their data riches.’ For many of these platforms, the quality of the data is of less interest than their quantity and diversity. Every action performed by a user, no matter how minute, is useful for reconfiguring algorithms and optimising processes. Such is the importance of data that many companies could make all of their software open-source and still maintain their dominant position due to their data. Unsurprisingly, then, these companies have been prolific purchasers and developers of assets that enable them to expand their capacity for gaining information. Mergers relating to big data, for instance, have doubled between 2008 and 2013.

  How seriously should we take this? My instinct is to say ‘not very’. The notion that all data is of potential value is licensing massive investment and expansion, entrenching the position of precisely those who are speaking most authoritatively in relation to this transition. To be sceptical of a gold rush doesn’t mean you’re denying the existence of gold, it just means you’re sceptical about the exuberance surrounding the search and how it is being deployed by some to benefit their own self-interest. The distinction between the ontology of data and the ideology of data matters.

   
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  Mark 2:38 pm on July 17, 2016 Permalink | Reply
  Tags: About Me ( 28 ), Archive ( 1,263 ), Archiving ( 115 ), big data ethics, data ethics, data science, data science ethics, Digital Capitalism and Digital Social Science ( 128 )   

  Interesting material on data science ethics 

  1. The Cambridge Ethics of Big Data Project
  2. Data ethics Case Studies
  3. Public dialogue on the ethics of data science in government
  4. Ipsos MORI data science ethics dialogue
  5. Data Science Ethics MOOC
  6. Code of conduct at the Data Science Association
   
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  Mark 3:27 pm on June 16, 2016 Permalink | Reply
  Tags: Archive ( 1,263 ), data science, Digital Capitalism and Digital Social Science ( 128 ), human analytics, human resources ( 3 ), recruitment ( 2 ), The Intensification of Work ( 107 ), Thinking ( 628 )   

  Credentials, recruitment and big data  

  A really interesting section in Ross Perlin’s Intern Nation discusses the role credentials play in recruitment as proxies for sought qualities in potential employees. Once this process of credentialisation-as-filtering is embedded in organisations, surely data science (‘human analytics’) is the logical next step? From loc 2482:

  The way job markets actually work, argue some economists, is that graduating from college or completing an internship, regardless of the skills learned, sends a signal to employers, most of whom have little ability to gauge the human capital of a prospective hire. “We have an information problem in the society and the problem of allocating the right people to the right jobs,” writes economist Michael Spence, whose early work on “signaling” has been as influential in its own way as Becker’s theory of human capital. At first, employers may have to guess whether any given signal—a high GPA, for instance—will make for a useful predictor of future success, but Spence posits “a feedback loop” in which employer expectations are revised over time, as the productivity of different types of workers reveals itself.

   
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  Mark 6:02 pm on May 15, 2016 Permalink | Reply
  Tags: Archive ( 1,263 ), data ( 20 ), data science, Digital Capitalism and Digital Social Science ( 128 ), Digital Social Science ( 56 ), empiricism ( 3 ), facts ( 4 ), ontology of data ( 3 ), philosophy of technology ( 26 ), positivism ( 3 ), Thinking ( 628 )   

  Data Fetishism 

  I find this argument from loc 270 of Rob Kitchin’s The Data Revolution extremely compelling. It reminds me of Roy Bhaskar’s argument about the fetishisation of facts from his Reclaiming Reality. This is what Kitchin says:

  Moreover, just as we think of bricks and mortar as simple building blocks rather than elements that are made within factories by companies bound within logistical, financial, legal and market concerns, and are distributed, stored and traded, so we largely do with data. Consequently, when data are the focus of enquiry it is usually to consider, in a largely technical sense, how they should be generated and analysed, or how they can be leveraged into insights and value, rather than to consider the nature of data from a more conceptual and philosophical perspective.

  This “conceptual and philosophical perspective” is a necessary, though insufficient, condition for a critical one. Only if we consider the social ontology of digital data does it become possible to reflexively incorporate critique of the originating platforms into the digital social science that those platforms facilitate.

   
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  Mark 7:49 am on April 17, 2016 Permalink
  Tags: data science, Digital Capitalism and Digital Social Science ( 128 )   

  The Future of Data Science 

   
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  Mark 6:48 pm on March 24, 2016 Permalink
  Tags: Archive ( 1,263 ), data science, Digital Capitalism and Digital Social Science ( 128 ), Digital Social Science ( 56 ), Interested ( 129 ), social computing   

  Data science meets social sciences: Understanding origins, means and consequences of social computing 

  I wish I could take part in this – it looks fantastic:

  Call for Submissions and Registration

  We would like to invite submissions for the 2016 Social Study of IT Open Research Forum (SSIT ORF) Workshop at the London School of Economics on the 19th of May 2016. Aiming at PhD and junior researchers, this year’s workshop will investigate the variety of data centric research in the social sciences.

  Arguably, we live in the age of the data revolution. The wave of data is redefining what we study, how we study it and how we theorise. Already acclaimed by some as a paradigmatic shift in science, the data revolution and data science receive considerable attention within the social sciences community. As such, this year’s Social Study of IT Open Research Forum of the Department of Management, LSE, aims at presenting research in progress in this area. If you are researching big data and analytics, this is the place for you to talk about your research, discuss potential stumbling blocks and hear from other participants who may be approaching similar research questions from different theoretical or methodological perspectives.

  A significant feature of the Open Research Forum is its informality. While we discourage the use of slides, we will focus on panel presentations with plenty of time to focus on emerging questions and issues. You do not need to bring polished research, but rather to briefly present your topic, research question, theory, methodology and tentative or expected findings and engage in discussion with fellow researchers.

  Registrations for the Open Research Forum are currently taking place. If you are interested in presenting, please submit a summary (500-1000 words) of the work or research issues you would like to present and discuss to SSIT-ORF@lse.ac.uk and register here. If you are interested in participating without a presentation, you can register to attend here. Deadline for submissions is the 14th of April 2016. Successful participants will be notified by the 22nd of April 2016.

  https://orflse.wordpress.com/

   
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  Mark 1:22 pm on February 10, 2016 Permalink
  Tags: Archive ( 1,263 ), behavioural science ( 2 ), data science, Digital Capitalism and Digital Social Science ( 128 ), Digital Social Science ( 56 ), natural science, naturalism ( 2 ), Thinking ( 628 )   

  Contempt for the social sciences and the new positivism 

  I’m a bit preoccupied by this paragraph from Philip Moriarty’s recent post about the invocation of quantum physics in the social sciences. I’d assumed this was true, inferring from things I’d seen others say and things I’d seen them not say, but I’ve never seen it acknowledged so bluntly before:

  Social science is important – it provides key insights into human behaviour and addresses questions that are beyond the scope of the physical sciences. I enjoy interacting and collaborating with my colleagues in social science both at Nottingham and elsewhere and gain a great deal from our discussions. But I’ll be brutally honest. I know for a fact that there are many in the “hard” sciences (and elsewhere) who would argue that the funding of social science is a waste of money and that it could be much better spent elsewhere. Misappropriating ideas from quantum mechanics in an attempt to ride on the coat-tails of the (highly successful) intellectual framework underpinning physics does social science no favours at all.

  https://muircheart.wordpress.com/2016/02/06/were-flattered-but-enough-of-the-physics-envy-its-embarrassing-us-all/

  To what extent should the imperialistic tendencies of contemporary data-driven behavioural science be read as, at least in part, expressions of this attitude? Can you be motivated to use the computational tools of the contemporary natural sciences to ‘fix’ the social sciences unless you quite straight forwardly regard them as broken?

   
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  Mark 10:50 pm on January 22, 2016 Permalink
  Tags: data science, Digital Capitalism and Digital Social Science ( 128 ), google ( 25 ), The Political Economy of Digital Capitalism ( 224 ), The Technological History of Digital Capitalism ( 137 )   

  Eric Schmidt on the century of the data scientists 

  From How Google Works loc 2000-2017:

  Stats are sexy. Deal with it. The sexiest jobs in the Internet Century will involve statistics, and not just in a parallel geeky fantasy world. Hal Varian notes that it is always a good idea for individuals to build expertise in areas that complement things that are getting cheap, and data, along with computing power to crunch it, is definitely getting cheap. We are in the era of big data, and big data needs statisticians to make sense of it. The democratization of data means that those who can analyze it well will win. Data is the sword of the twenty-first century, those who wield it well, the samurai. So start sharpening that blade, uruwashii, 114 and take statistics. “But I’m not a numbers person!” we hear some of you whining, especially you in the back with the magenta shirt. Don’t worry, there is hope. Asking the questions and interpreting the answers is as important a skill as coming up with the answers themselves. No matter your business, learn how the right data, crunched the right way, will help you make better decisions. Learn which questions to ask the people who are good with numbers and how to make the best use of their replies. Even if you aren’t a numbers person, you can learn how to use the numbers to get smarter.

   
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  Mark 7:02 pm on January 2, 2016 Permalink
  Tags: business science, business scientists, data science, data scientists ( 3 ), Digital Capitalism and Digital Social Science ( 128 )   

  the demand for data science 

  An interesting article about the increasing demand for data scientists and the centrality which corporate imperatives should have on data science syllabi. How will data science change as it scales? It seems inevitable that undergraduate programmes will play a crucial role in meeting this demand:

  o what’s the problem? We have a global talent shortage, and the demand for data scientists continues to grow rapidly, far outpacing the anemic growth in supply. A McKinsey study predicts that by 2018 the number of data science jobs in the United States alone will exceed 490,000, but there will be fewer than 200,000 available data scientists to fill these positions. Globally, demand for data scientists is projected to exceed supply by more than 50 percent by 2018.

  The need for experts trained in extracting insights from data is more important than ever.

  The global talent drought may be partially caused by the dearth of universities that offer data science programs dedicated to preparing the next generation of data scientists. Fewer than one-third of U.S. News & World Report’s Top 100 Global Universities offer degrees in data science. Of the 29 universities that offer data science programs, a mere six make them available to undergraduates (the rest are reserved for graduate students).

  Moreover, the average cohort size for one of these data science programs is just 23 students. The small cohort size may be partially attributed to the fact that most of these data science programs tend to be offered to graduate students, and sizes of graduate programs tend to be smaller. Still, at 23 students per cohort, we are unlikely to make a meaningful dent in closing the global data science talent gap.

  An analysis of the curricula for these data science programs also suggests cause for concern. Most are focused almost exclusively on the computer engineering aspects of data science, with course titles such as Software Design, Parallel Computing and Software Development.

  Largely missing from the data science curricula at many of these universities are courses in statistical analysis, insights and strategy. This oversight may have serious consequences for graduating students and their future employers.

  Without training in these other areas, data scientists may be capable of designing an algorithm that is mathematically elegant, but doesn’t make strategic sense for the business. They also may not know how to design an experiment to determine whether the algorithm is effective. In other words, computer science skills alone are insufficient for success as a data scientist in today’s marketplace.

  Furthermore, many data science programs lack courses that help students apply their technical data science skills to fields such as marketing, operations, product development and supply chain, and industries such as energy, bioinformatics, transportation and healthcare.

  http://techcrunch.com/2015/12/31/how-to-stem-the-global-shortage-of-data-scientists/

   
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  Mark 8:36 am on December 16, 2015 Permalink
  Tags: computational politics ( 3 ), data science, Digital Capitalism and Digital Social Science ( 128 ), Digital Elections, Party Politics and Diplomacy ( 64 ), elections ( 9 ), party politics ( 4 ), Shadow Mobilization, Astroturfing and Manipulation ( 52 )   

  the new frontier of computational politics 

  A really interesting report on the Guardian about the data scientists working within Ted Cruz’s campaign. As Zeynap Tufekci argues, the potential implications of computational politics are remarkably worrying. We urgently need ethical regulation of data science because otherwise this will prove to be the thin end of the wedge:

  Ted Cruz’s presidential campaign is using psychological data based on research spanning tens of millions of Facebook users, harvested largely without their permission, to boost his surging White House run and gain an edge over Donald Trump and other Republican rivals, the Guardian can reveal.

  A little-known data company, now embedded within Cruz’s campaign and indirectly financed by his primary billionaire benefactor, paid researchers at Cambridge University to gather detailed psychological profiles about the US electorate using a massive pool of mainly unwitting US Facebook users built with an online survey.

  As part of an aggressive new voter-targeting operation, Cambridge Analytica – financially supported by reclusive hedge fund magnate and leading Republican donor Robert Mercer – is now using so-called “psychographic profiles” of US citizens in order to help win Cruz votes, despite earlier concerns and red flags from potential survey-takers.

  Documents seen by the Guardian have uncovered longstanding ethical and privacy issues about the way academics hoovered up personal data by accessing a vast set of US Facebook profiles, in order to build sophisticated models of users’ personalities without their knowledge.

  In the race to advance data-driven electioneering strategies pioneered by successive Obama campaigns, Cruz has turned to Cambridge Analytica for its unparalleled offering of psychological data based on a treasure trove of Facebook “likes”, allowing it to match individuals’ traits with existing voter datasets, such as who owned a gun.

  http://firstmonday.org/article/view/4901/4097