Shareveillance: Subjectivity between open and closed data

Curated on Posted on by Stefaan Verhulst

Clare Birchall in Big Data and Society: “This article attempts to question modes of sharing and watching to rethink political subjectivity beyond that which is enabled and enforced by the current data regime. It identifies and examines a ‘shareveillant’ subjectivity: a form configured by the sharing and watching that subjects have to withstand and enact in the contemporary data assemblage. Looking at government open and closed data as case studies, this article demonstrates how ‘shareveillance’ produces an anti-political role for the public. In describing shareveillance as, after Jacques Rancière, a distribution of the (digital) sensible, this article posits a politico-ethical injunction to cut into the share and flow of data in order to arrange a more enabling assemblage of data and its affects. In order to interrupt shareveillance, this article borrows a concept from Édouard Glissant and his concern with raced otherness to imagine what a ‘right to opacity’ might mean in the digital context. To assert this right is not to endorse the individual subject in her sovereignty and solitude, but rather to imagine a collective political subjectivity and relationality according to the important question of what it means to ‘share well’ beyond the veillant expectations of the state.

Two questions dominate current debates at the intersection of privacy, governance, security, and transparency: How much, and what kind of data should citizens have to share with surveillant states? And: How much data from government departments should states share with citizens? Yet, these issues are rarely expressed in terms of ‘sharing’ in the way that I will be doing in this article. More often, when thought in tandem with the digital, ‘sharing’ is used in reference to either free trials of software (‘shareware’); the practice of peer-to-peer file sharing; platforms that facilitate the pooling, borrowing, swapping, renting, or selling of resources, skills, and assets that have come to be known as the ‘sharing economy’; or the business of linking and liking on social media, which invites us to share our feelings, preferences, thoughts, interests, photographs, articles, and web links. Sharing in the digital context has been framed as a form of exchange, then, but also communication and distribution (see John, 2013; Wittel, 2011).

In order to understand the politics of open and opaque government data practices, which either share with citizens or ask citizens to share, I will extend existing commentaries on the distributive qualities of sharing by drawing on Jacques Rancière’s notion of the ‘distribution of the sensible’ (2004a) – a settlement that determines what is visible, audible, sayable, knowable and what share or role we each have within it. In the process, I articulate ‘sharing’ with ‘veillance’ (veiller ‘to watch’ is from the Latin vigilare, from vigil, ‘watchful’) to turn the focus from prevalent ways of understanding digital sharing towards a form of contemporary subjectivity. What I call ‘shareveillance’ – a state in which we are always already sharing; indeed, in which any relationship with data is only made possible through a conditional idea of sharing – produces an anti-politicised public caught between different data practices.

I will argue that both open and opaque government data initiatives involve, albeit differently pitched, forms of sharing and veillance. Government practices that share data with citizens involve veillance because they call on citizens to monitor and act upon that data – we are envisioned (‘veiled’ and hailed) as auditing and entrepreneurial subjects. Citizens have to monitor the state’s data, that is, or they are expected to innovate with it and make it profitable. Data sharing therefore apportions responsibility without power. It watches citizens watching the state, delimiting the ways in which citizens can engage with that data and, therefore, the scope of the political per se….(More)”.

Is Open Data the Death of FOIA?

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Beth Noveck at the Yale Law Journal: “For fifty years, the Freedom of Information Act (FOIA) has been the platinum standard for open government in the United States. The statute is considered the legal bedrock of the public’s right to know about the workings of our government. More than one hundred countries and all fifty states have enacted their own freedom of information laws. At the same time, FOIA’s many limitations have also become evident: a cumbersome process, delays in responses, and redactions that frustrate journalists and other information seekers. Politically-motivated nuisance requests bedevil government agencies.With over 700,000 FOIA requests filed every year, the federal government faces the costs of a mounting backlog.

In recent years, however, an entirely different approach to government transparency in line with the era of big data has emerged: open government data. Open government data —generally shortened to open data—has many definitions but is generally considered to be publicly available information that can be universally and readily accessed, used, and redistributed free of charge in digital form. Open data is not limited to statistics, but also includes text such as the United States Federal Register, the daily newspaper of government, which was released as open data in bulk form in 2010.

To understand how significant the open data movement is for FOIA, this Essay discusses the impact of open data on the institutions and functions of government and the ways open data contrasts markedly with FOIA. Open data emphasizes the proactive publication of whole classes of information. Open data includes data about the workings of government but also data collected by the government about the economy and society posted online in a centralized repository for use by the wider public, including academic users seeking information as the basis for original research and commercial users looking to create new products and services. For example, Pixar used open data from the United States Geological Survey to create more realistic detail in scenes from its movie The Good Dinosaur.

By contrast, FOIA promotes ex post publication of information created by the government especially about its own workings in response to specific demands by individual requestors. I argue that open data’s more systematic and collaborative approach represents a radical and welcome departure from FOIA because open data concentrates on information as a means to solve problems to the end of improving government effectiveness. Open data is legitimated by the improved outcomes it yields and grounded in a theory of government effectiveness and, as a result, eschews the adversarial and ad hoc FOIA approach. Ultimately, however, each tactic offers important complementary benefits. The proactive information disclosure regime of open data is strengthened by FOIA’s rights of legal enforcement. Together, they stand to become the hallmark of government transparency in the fifty years ahead….(More)”.

New Institute Pushes the Boundaries of Big Data

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Press Release: “Each year thousands of genomes are sequenced, millions of neuronal activity traces are recorded, and light from hundreds of millions of galaxies is captured by our newest telescopes, all creating datasets of staggering size. These complex datasets are then stored for analysis.

Ongoing analysis of these information streams has illuminated a problem, however: Scientists’ standard methodologies are inadequate to the task of analyzing massive quantities of data. The development of new methods and software to learn from data and to model — at sufficient resolution — the complex processes they reflect is now a pressing concern in the scientific community.

To address these challenges, the Simons Foundation has launched a substantial new internal research group called the Flatiron Institute (FI). The FI is the first multidisciplinary institute focused entirely on computation. It is also the first center of its kind to be wholly supported by private philanthropy, providing a permanent home for up to 250 scientists and collaborating expert programmers all working together to create, deploy and support new state-of-the-art computational methods. Few existing institutions support the combination of scientists and programmers, instead leaving programming to relatively impermanent graduate students and postdoctoral fellows, and none have done so at the scale of the Flatiron Institute or with such a broad scope, at a single location.

The institute will hold conferences and meetings and serve as a focal point for computational science around the world….(More)”.

Who Is Doing Computational Social Science?

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Trends in Big Data Research, a Sage Whitepaper: “Information of all kinds is now being produced, collected, and analyzed at unprecedented speed, breadth, depth, and scale. The capacity to collect and analyze massive data sets has already transformed fields such as biology, astronomy, and physics, but the social sciences have been comparatively slower to adapt, and the path forward is less certain. For many, the big data revolution promises to ask, and answer, fundamental questions about individuals and collectives, but large data sets alone will not solve major social or scientific problems. New paradigms being developed by the emerging field of “computational social science” will be needed not only for research methodology, but also for study design and interpretation, cross-disciplinary collaboration, data curation and dissemination, visualization, replication, and research ethics (Lazer et al., 2009). SAGE Publishing conducted a survey with social scientists around the world to learn more about researchers engaged in big data research and the challenges they face, as well as the barriers to entry for those looking to engage in this kind of research in the future. We were also interested in the challenges of teaching computational social science methods to students. The survey was fully completed by 9412 respondents, indicating strong interest in this topic among our social science contacts. Of respondents, 33 percent had been involved in big data research of some kind and, of those who have not yet engaged in big data research, 49 percent (3057 respondents) said that they are either “definitely planning on doing so in the future” or “might do so in the future.”…(More)”

The ethical impact of data science

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Theme issue of Phil. Trans. R. Soc. A compiled and edited by Mariarosaria Taddeo and Luciano Floridi: “This theme issue has the founding ambition of landscaping data ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing and use), algorithms (including artificial intelligence, artificial agents, machine learning and robots) and corresponding practices (including responsible innovation, programming, hacking and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values). Data ethics builds on the foundation provided by computer and information ethics but, at the same time, it refines the approach endorsed so far in this research field, by shifting the level of abstraction of ethical enquiries, from being information-centric to being data-centric. This shift brings into focus the different moral dimensions of all kinds of data, even data that never translate directly into information but can be used to support actions or generate behaviours, for example. It highlights the need for ethical analyses to concentrate on the content and nature of computational operations—the interactions among hardware, software and data—rather than on the variety of digital technologies that enable them. And it emphasizes the complexity of the ethical challenges posed by data science. Because of such complexity, data ethics should be developed from the start as a macroethics, that is, as an overall framework that avoids narrow, ad hoc approaches and addresses the ethical impact and implications of data science and its applications within a consistent, holistic and inclusive framework. Only as a macroethics will data ethics provide solutions that can maximize the value of data science for our societies, for all of us and for our environments….(More)”

Table of Contents:

  • The dynamics of big data and human rights: the case of scientific research; Effy Vayena, John Tasioulas
  • Facilitating the ethical use of health data for the benefit of society: electronic health records, consent and the duty of easy rescue; Sebastian Porsdam Mann, Julian Savulescu, Barbara J. Sahakian
  • Faultless responsibility: on the nature and allocation of moral responsibility for distributed moral actions; Luciano Floridi
  • Compelling truth: legal protection of the infosphere against big data spills; Burkhard Schafer
  • Locating ethics in data science: responsibility and accountability in global and distributed knowledge production systems; Sabina Leonelli
  • Privacy is an essentially contested concept: a multi-dimensional analytic for mapping privacy; Deirdre K. Mulligan, Colin Koopman, Nick Doty
  • Beyond privacy and exposure: ethical issues within citizen-facing analytics; Peter Grindrod
  • The ethics of smart cities and urban science; Rob Kitchin
  • The ethics of big data as a public good: which public? Whose good? Linnet Taylor
  • Data philanthropy and the design of the infraethics for information societies; Mariarosaria Taddeo
  • The opportunities and ethics of big data: practical priorities for a national Council of Data Ethics; Olivia Varley-Winter, Hetan Shah
  • Data science ethics in government; Cat Drew
  • The ethics of data and of data science: an economist’s perspective; Jonathan Cave
  • What’s the good of a science platform? John Gallacher

 

What’s wrong with big data?

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James Bridle in the New Humanist: “In a 2008 article in Wired magazine entitled “The End of Theory”, Chris Anderson argued that the vast amounts of data now available to researchers made the traditional scientific process obsolete. No longer would they need to build models of the world and test them against sampled data. Instead, the complexities of huge and totalising datasets would be processed by immense computing clusters to produce truth itself: “With enough data, the numbers speak for themselves.” As an example, Anderson cited Google’s translation algorithms which, with no knowledge of the underlying structures of languages, were capable of inferring the relationship between them using extensive corpora of translated texts. He extended this approach to genomics, neurology and physics, where scientists are increasingly turning to massive computation to make sense of the volumes of information they have gathered about complex systems. In the age of big data, he argued, “Correlation is enough. We can stop looking for models.”

This belief in the power of data, of technology untrammelled by petty human worldviews, is the practical cousin of more metaphysical assertions. A belief in the unquestionability of data leads directly to a belief in the truth of data-derived assertions. And if data contains truth, then it will, without moral intervention, produce better outcomes. Speaking at Google’s private London Zeitgeist conference in 2013, Eric Schmidt, Google Chairman, asserted that “if they had had cellphones in Rwanda in 1994, the genocide would not have happened.” Schmidt’s claim was that technological visibility – the rendering of events and actions legible to everyone – would change the character of those actions. Not only is this statement historically inaccurate (there was plenty of evidence available of what was occurring during the genocide from UN officials, US satellite photographs and other sources), it’s also demonstrably untrue. Analysis of unrest in Kenya in 2007, when over 1,000 people were killed in ethnic conflicts, showed that mobile phones not only spread but accelerated the violence. But you don’t need to look to such extreme examples to see how a belief in technological determinism underlies much of our thinking and reasoning about the world.

“Big data” is not merely a business buzzword, but a way of seeing the world. Driven by technology, markets and politics, it has come to determine much of our thinking, but it is flawed and dangerous. It runs counter to our actual findings when we employ such technologies honestly and with the full understanding of their workings and capabilities. This over-reliance on data, which I call “quantified thinking”, has come to undermine our ability to reason meaningfully about the world, and its effects can be seen across multiple domains.

The assertion is hardly new. Writing in the Dialectic of Enlightenment in 1947, Theodor Adorno and Max Horkheimer decried “the present triumph of the factual mentality” – the predecessor to quantified thinking – and succinctly analysed the big data fallacy, set out by Anderson above. “It does not work by images or concepts, by the fortunate insights, but refers to method, the exploitation of others’ work, and capital … What men want to learn from nature is how to use it in order wholly to dominate it and other men. That is the only aim.” What is different in our own time is that we have built a world-spanning network of communication and computation to test this assertion. While it occasionally engenders entirely new forms of behaviour and interaction, the network most often shows to us with startling clarity the relationships and tendencies which have been latent or occluded until now. In the face of the increased standardisation of knowledge, it becomes harder and harder to argue against quantified thinking, because the advances of technology have been conjoined with the scientific method and social progress. But as I hope to show, technology ultimately reveals its limitations….

“Eroom’s law” – Moore’s law backwards – was recently formulated to describe a problem in pharmacology. Drug discovery has been getting more expensive. Since the 1950s the number of drugs approved for use in human patients per billion US dollars spent on research and development has halved every nine years. This problem has long perplexed researchers. According to the principles of technological growth, the trend should be in the opposite direction. In a 2012 paper in Nature entitled “Diagnosing the decline in pharmaceutical R&D efficiency” the authors propose and investigate several possible causes for this. They begin with social and physical influences, such as increased regulation, increased expectations and the exhaustion of easy targets (the “low hanging fruit” problem). Each of these are – with qualifications – disposed of, leaving open the question of the discovery process itself….(More)

Environmental Law, Big Data, and the Torrent of Singularities

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Essay by William Boyd: “How will big data impact environmental law in the near future? This Essay imagines one possible future for environmental law in 2030 that focuses on the implications of big data for the protection of public health from risks associated with pollution and industrial chemicals. It assumes the perspective of an historian looking back from the end of the twenty-first century at the evolution of environmental law during the late twentieth and early twenty-first centuries.

The premise of the Essay is that big data will drive a major shift in the underlying knowledge practices of environmental law (along with other areas of law focused on health and safety). This change in the epistemic foundations of environmental law, it is argued, will in turn have important, far-reaching implications for environmental law’s normative commitments and for its ability to discharge its statutory responsibilities. In particular, by significantly enhancing the ability of environmental regulators to make harm more visible and more traceable, big data will put considerable pressure on previous understandings of acceptable risk across populations, pushing toward a more singular and more individualized understanding of harm. This will raise new and difficult questions regarding environmental law’s capacity to confront and take responsibility for the actual lives caught up in the tragic choices it is called upon to make. In imagining this near future, the Essay takes a somewhat exaggerated and, some might argue, overly pessimistic view of the implications of big data for environmental law’s efforts to protect public health. This is done not out of a conviction that such a future is likely, but rather to highlight some of the potential problems that may arise as big data becomes a more prominent part of environmental protection. In an age of data triumphalism, such a perspective, it is hoped, may provide grounds for a more critical engagement with the tools and knowledge practices that inform environmental law and the implications of those tools for environmental law’s ability to meet its obligations. Of course, there are other possible futures, and big data surely has the potential to make many positive contributions to environmental protection in the coming decades. Whether it will do so will depend in no small part on the collective choices we make to manage these new capabilities in the years ahead….(More)”

Power to the People: Addressing Big Data Challenges in Neuroscience by Creating a New Cadre of Citizen Neuroscientists

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Jane Roskams and Zoran Popović in Neuron: “Global neuroscience projects are producing big data at an unprecedented rate that informatic and artificial intelligence (AI) analytics simply cannot handle. Online games, like Foldit, Eterna, and Eyewire—and now a new neuroscience game, Mozak—are fueling a people-powered research science (PPRS) revolution, creating a global community of “new experts” that over time synergize with computational efforts to accelerate scientific progress, empowering us to use our collective cerebral talents to drive our understanding of our brain….(More)”

The Risk to Civil Liberties of Fighting Crime With Big Data

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Quentin Hardy  in the New York Times: “…Sharing data, both among the parts of a big police department and between the police and the private sector, “is a force multiplier,” he said.

Companies working with the military and intelligence agencies have long practiced these kinds of techniques, which the companies are bringing to domestic policing, in much the way surplus military gear has beefed upAmerican SWAT teams.

Palantir first built up its business by offering products like maps of social networks of extremist bombers and terrorist money launderers, and figuring out efficient driving routes to avoid improvised explosive devices.

Palantir used similar data-sifting techniques in New Orleans to spot individuals most associated with murders. Law enforcement departments around Salt Lake City used Palantir to allow common access to 40,000 arrest photos, 520,000 case reports and information like highway and airport data — building human maps of suspected criminal networks.

People in the predictive business sometimes compare what they do to controlling the other side’s “OODA loop,” a term first developed by a fighter pilot and military strategist named John Boyd.

OODA stands for “observe, orient, decide, act” and is a means of managing information in battle.

“Whether it’s war or crime, you have to get inside the other side’s decision cycle and control their environment,” said Robert Stasio, a project manager for cyberanalysis at IBM, and a former United States government intelligence official. “Criminals can learn to anticipate what you’re going to do and shift where they’re working, employ more lookouts.”

IBM sells tools that also enable police to become less predictable, for example, by taking different routes into an area identified as a crime hotspot. It has also conducted studies that show changing tastes among online criminals — for example, a move from hacking retailers’ computers to stealing health care data, which can be used to file for federal tax refunds.

But there are worries about what military-type data analysis means for civil liberties, even among the companies that get rich on it.

“It definitely presents challenges to the less sophisticated type of criminal,but it’s creating a lot of what is called ‘Big Brother’s little helpers,’” Mr.Bowman said. For now, he added, much of the data abundance problem is that “most police aren’t very good at this.”…(More)’

Big Data Is Not a Monolith

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Book edited by Cassidy R. Sugimoto, Hamid R. Ekbia and Michael Mattioli: “Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identify linguistic correlations in large corpuses of texts. This book examines big data not as an undifferentiated whole but contextually, investigating the varied challenges posed by big data for health, science, law, commerce, and politics. Taken together, the chapters reveal a complex set of problems, practices, and policies.

The advent of big data methodologies has challenged the theory-driven approach to scientific knowledge in favor of a data-driven one. Social media platforms and self-tracking tools change the way we see ourselves and others. The collection of data by corporations and government threatens privacy while promoting transparency. Meanwhile, politicians, policy makers, and ethicists are ill-prepared to deal with big data’s ramifications. The contributors look at big data’s effect on individuals as it exerts social control through monitoring, mining, and manipulation; big data and society, examining both its empowering and its constraining effects; big data and science, considering issues of data governance, provenance, reuse, and trust; and big data and organizations, discussing data responsibility, “data harm,” and decision making….(More)”