Theorising the relationship between data and agency

My notes on Kennedy, H., Poell, T., & van Dijck, J. (2015). Data and agency. Big Data & Society. https://doi.org/10.1177/2053951715621569

This short paper is the editorial for a special section of big data & society, exploring the role of agency in relation to data. Their starting point is the tendency in the literature to focus on how datafication – which they define as rendering as data what was not previously quantified – can be used for surveillance and control, though itself an expression of the agency of elites. What gets lost are the “small-scale public organisations, community groups and activists are experimenting with the possibilities of datafication, pursuing objectives which are distinct from those of big brother’s uses of Big Data” (1). Data captures the situation of users but it is also often fed back to users, allowing them to orientate themselves in the world. It is for this reason that they delve into the structure/agency debate, in search of theoretical resources to make sense of the relationship between data and agency. They see the tension between control and resistance as an iteration of the structure/agency question. This is how they summarise the papers in the issue. From pg 3:

The five contributions aim to consider the extent of the dominance of the structures of datafication, the possibility of agency, and the spaces in between. At the same time, the contributions seek to combine critical perspectives on datafication with the perspectives of actors within data mining practices. The aim is to enrich our understanding of data and datafication, by bringing together structural analyses with recognition of individual agency in the context of these structures.

Interestingly, they draw a parallel between the relative denial of reflexivity you find in a thinker like Bourdieu with digital theories which stress the role of infrastructure and computation. But I thought this section was otherwise quite weak, drawing a contrast on the basis that “acting with agency is not necessarily reflexive or moral; it is not necessarily good” (3) for Bourdieu, whereas it seems pretty evident to me that these debates were about the character of social reproduction not about the cultural politics of reflexivity.

I like the brief discussion of the ontological implications of the term datafication, in relation to the paper by Pybus et al. This helped me articulate why I’ve always found the term datafication unhelpful: by foreground the outcome and nominalising the process, it obscures the agency of those who are doing the datafying and those who are on the receiving end of it. It ends on pg 6 with a very helpful overview of key questions concerning how citizens can be empowered in relation to data:

To participate in datafied social, political, cultural and civic life, ordinary people need to under- stand what happens to their data, the consequences of data analysis, and the ways in which data-driven oper- ations affect us all. Can ordinary people do the same things with their data as corporations and organisa- tions? Citizens tend to give away data to platforms that facilitate their daily communication, automatically donating personal data about their health and fitness to apps like Runkeeper or Strava or sharing experiences of illness on healthcare tools like CureTogether. But what do they know about the data streams they help to gen- erate? How conscious are users of the ways in which their data are technically steered, repurposed, and resold? Platform sites cultivate certain styles of use, but actual users help steer how data become valuable. The distinction between the ‘technological unconscious’ (a term used by Beer, 2009) and the ‘conscious user’ becomes relevant here: how much do users know about data streams and their abilities to control them? The Quantified Self movement is one example of individuals attempting to take ownership of their own data (for example Nafus and Sherman, 2014), although critics point out that corporations ultimately benefit from these data-gathering practices (Crawford et al., 2015).

This is coupled with another interesting observation that digital workers designing these systems are often assumed to be responsible, as if they were all powerful.

How do data come to matter?

My notes on Lupton, D. (2018). How do data come to matter? Living and becoming with personal data. Big Data & Society, 5(2), 2053951718786314.

In this paper, Deborah Lupton extends her work on the quantified self into a broader theorisation of how people come to live with data. It foregrounds the voluntary dimension of this process, in which “many novel digital technologies offer any interested person the opportunity to document, monitor and measure details of their bodies”, equipping them with capacities which were previously confined to specialised instruments and trained experts (1). These techniques render visible what was previously unseen. From 2:

Elements of their bodies that people may not otherwise have con- sidered to any great extent – the number of steps they take per day, their sleep patterns, the kilometres and geographical locations they move through, their brain waves, moods and so on – are brought into sharp relief.

This renders the body as a series of interlocking digitised elements which “demand new ways of interpreting these signs and signals of bodily function and movement” (2). Her focus is on the existential and epistemic predicament this confronts people with: how do we make sense of this information, determine its value and put it into practice? This takes place in a context where there is pervasive cultural pressure to know our bodies better and live in a way deemed more efficient. Her focus is on how this data is experienced and understood in everyday life.

This has been studied through the frame of data sense-making i.e. how people engage with and learn from information. It has been tied to data literacy, a concern for capacities to select, analyse, visualise and learn from data. Lupton highlights how these approaches tend to focus on cognitive and technical forms of interpretation, ignoring the role of the situated body as a means through which we learn. She uses the concept of data sense to this end, which incorporates the “entanglements of the digital sensors with the human senses in the process of sense-making” (3) with the body as the site of sensation and response.

This project draws on a range of approaches from agential realism, new materialism and the anthropology of material culture. They share a more-than-human approach which “demands that the human subject is always considered permeable and open to the material world rather than closed-off and contained” (4). They share the following characterised described on 5:

an approach that recognises that humans and non- humans are entangled in hybrid, unstable and generative ways;

the importance of considering the distributed agency and vital capacities (‘thing-power’) of human non-human assemblages;

an emphasis on the embodied, sensory and otherwise material nature of meaning, knowing, growing, perceiving and making as part of human embodiment;

the changing meanings of artefacts as they move into different assemblages and the work required to articulate these assemblages; and

the importance of identifying and tracing the ways in which humans and nonhumans are intermeshed, the enactments and practices that are involved, and the effects of these on human lives.

These inform Lupton’s conception of a human-data assemblage within which data learns about humans but humans in turn “may find themselves asking to what extent their data speak for them, and to what extent their data are different from other elements of embodiment and selfhood” (5). Digital devices and software for personal data necessarily seek to make data intelligible to users. Through such intelligibility personal data has agency in relation users, exercising an influencer over their behaviour and leading them to do things on the basis of these new understandings. But data can also ossify if it’s not found useful or actionable, freezing into a latent state which could be rendered lively again at a later date. Her work on self-tracking provides an illustrative example of why these distinctions matter. Described on 6-7:

My research participants often described collecting and reviewing data about their bodies as generating agential capacities that are suffused with affect. These data can motivate them, encourage them to move their bodies more, persist with weight-loss efforts or self-management of chronic conditions. The ‘numbers’ can make them feel good if they demonstrate that people are achieving goals set for themselves, or if the data demonstrate good health or higher levels of fitness. Positive feelings can be generated by the buzzes, flashing lights, badges and other notifications that communicate a goal has been achieved. Alternatively, however, biometric data can have demoralising effects, generating disappointment, frustration, guilt and anger. Notifications can be experienced as annoying or pestering, making unreasonable demands.

To make sense of data involves “connecting the metrics with the lived sensory experiences of one’s body and the other elements that are important in data sense-making” (7). This is a contextualised process of building a human-data assemblage, shaped by the environment but also contributing to it. This stress on the meaning of the context is crucial if we want to understand, for instance, how particular professional groups might engage in specific ways with personal data. As Deborah puts it on 7:

When people review their data, they actively relate them to the contexts in which they were generated. People consider such aspects as the time of day, the weather, how their bodies felt, whether they were lacking sleep, were hungry, feeling stressed, drank too much the night before, what place and space they were inhabiting or moving through when the information were generated.

Oddly though I think an individual is reproduced here, in spite of the theoretical sources. My point of departure is the claim that personal “data are meaningful because they are about and for them” (8) and I think this insufficient to account for mattering in Andrew Sayer’s sense. Mattering always points beyond the relationship between the agent and what matters, something which I’m not sure the concept of the assemblage can account for because it squeezed human and data together in a dance of co-constitution. This is something I’ve tried to analyse in Katherine Hayles work and I’m thinking I need to seriously explore the issue I persistently see in co-constitution theorising, which I take to be a novel form of central conflationism in Margaret Archer’s sense.

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.

The history of data-as-rhetoric

From Daniel Rosenberg’s essay in Raw Data Is An Oxymoron, loc 916. What further developments are we beginning to see in the meaning of ‘data’ in a digitalised context? The author’s point is that data is not associated with veracity, such that inaccurate data is still data, but I wonder if I’ve misunderstood this because it seems so obviously misleading to me.

This observation is supported by the numbers but not generated by them: from the beginning, data was a rhetorical concept. Data means—and has meant for a very long time—that which is given prior to argument. As a consequence, the meaning of data must always shift with argumentative strategy and context—and with the history of both. The rise of modern economics and empirical natural science created new conditions of argument and new assumptions about facts and evidence. And the histories of those terms and others in the same family nicely illustrate the larger epistemological developments.

The history of data is connected to these other histories in very important ways, but in equally important ways, it remains an outlier. Curiously, the preexisting semantic structure of the term “data” made it especially flexible in these shifting epistemological and semantic contexts. Without changing meaning, during the eighteenth century data changed connotation. It went from being reflexively associated with those things that are outside of any possible process of discovery to being the very paradigm of what one seeks through experiment and observation.

I’m interested in how this supports the implicitly critical theory of data scientists: digital data reveals what people really do, rather than what they say they do. The given character of empirical data was formerly localised and now it’s seen to be generalised: given-ness suffuses the social world in a way that seemingly promises total legibility to those with sufficient literacy of the right sort.

A wonderful analogy by @Elinor_Carmi

I love the analogy offered by Elinor Carmi at the start of this excellent Open Democracy piece:

Yesterday I walked to the supermarket, like I do every Tuesday morning. All of a sudden I started noticing a few people starting to follow me. I try to convince myself that it is probably just my imagination, and carry on walking. After a few minutes, I cross the road and make another turn, but then I look behind me and see that now there are dozens of people starting to follow me, taking pictures of me and writing rapidly, documenting my every move. After a couple more steps, they became hundreds. My heart was racing, I could hardly breathe, and I started to panic. Freaking out, I shouted at them, “Who are you? What do you want from me?” I tried to get a clearer view of this huge group – some looked a bit familiar but I didn’t remember where I’d seen them before. They shouted back at me, “Don’t worry, we don’t really know who you are, we just need some information on you, so we can show you different ads on billboards”. Puzzled by their response I scream, “What do you mean you don’t know who I am!? You know my gender, skin/eyes/hair color, height, weight, where I live, the clothes and glasses I wear, that I have 10 piercing in one ear and that I shop at Sainsbury on Tuesday mornings!” They smile and try to reassure me, “But we don’t know your NAME, silly! So stop being so paranoid, we do this to everyone walking on the street, it’s public space you know…”.

This scenario might seem science fiction to some people, a dystopian reality, horror film or a South Park episode. But for the others that recognise this situation, this is actually what happens every day when you browse the internet.

https://www.opendemocracy.net/digitaliberties/elinor-carmi/whose-data-is-it-anyway

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.

The ontology of data

An astonishing lucid introduction from loc 257 of Rob Kitchin’s The Data Revolution:

Data are commonly understood to be the raw material produced by abstracting the world into categories, measures and other representational forms –numbers, characters, symbols, images, sounds, electromagnetic waves, bits –that constitute the building blocks from which information and knowledge are created. Data are usually representative in nature (e.g., measurements of a phenomena, such as a person’s age, height, weight, colour, blood pressure, opinion, habits, location, etc.), but can also be implied (e.g., through an absence rather than presence) or derived (e.g., data that are produced from other data, such as percentage change over time calculated by comparing data from two time periods), and can be either recorded and stored in analogue form or encoded in digital form as bits (binary digits).

Digital methods/data activism summer school

This looks like a great opportunity:

**Call for participation**
The second week of the Summer School is dedicated to the study of ‘data activism’.

*What is data activism?*
With the diffusion of big data, citizens become increasingly aware of the critical role of information in modern societies. Today’s world is awash with data. Never before have we created such a quantity of data by and about people, things, and their interactions. While this data has captured the imagination of governments and corporations alike, people are also increasingly responding to this new technological landscape.
From open data initiatives to privacy enhancing technologies, a growing number of people are developing new tools and practices in response to massive data collection and availability. People take advantage of the possibilities of data for civic engagement, advocacy, and campaigning (pro-active data activism). At the same time, people resist its harms through the development and use of encryption and free and open source alternatives to centralised software and online services (re-active data activism). Data activism is a signal of a more general change in perspectives and attitudes towards massive data collection. For more see data-activism.net.

*Experiments with methods*
Data activism emerges at the intersection of the social and technological dimensions of human action. This raises the question: how are we to understand and study this phenomenon? We think that it is important to experiment with methods. We want to investigate how people make use of data and interact with the socio-technical infrastructures that enable their circulation. This is especially relevant, since these are often complex, proprietary and opaque. For this purpose, we want to test and refine research approaches that enable the study of technological practices and infrastructures. This can involve configurations of digital methods and ethnographically informed traditions bridging media studies, science and technology studies (STS), informatics, and anthropology.
* Can we develop an approach to ‘software ethnography’, which traces and explores assemblages of data, infrastructures, technology designers and technology users?
* How can we learn how (big) data infrastructures actually function and are used in practice?
* Technology is always changing. How can we trace changes to the socio-technical infrastructures of tools over time?
* How can new software tools help us understand the workings of different kinds of data infrastructures? Can we develop tools to reverse-engineer algorithms, analytic techniques, or surveillance infrastructures?

*Format*
Participants will work 4 days in self-organised groups on a project to explore these questions, together with several invited subject matter experts.

*Output*
Outputs can include short research reports (wiki’s) describing process and results, tool prototypes, or creative interventions.

For questions, please contact info@data-activism.net

A Lockean case for the ownership of personal data

An interesting idea from Craig Lambert’s Shadow Work loc 3116 which deserves to be explored in greater depth:

As noted earlier, philosopher John Locke argued that labor creates property; taking his view, if your shadow work made some information, it is your possession. In fact, who owns your data—your informational body—may some day be as contentious an issue as the ownership of petroleum, water, or any other natural resource.

The Lives and Deaths of Data

This looks superb:

Open Track: The Lives and Deaths of Data

Convenors: Sabina Leonelli and Brian Rappert, Department of Sociology, Philosophy and Anthropology & Exeter Centre for the Study of the Life Sciences (Egenis), University of Exeter, UK (see also the Exeter Data Studies group: http://www.datastudies.eu)

Abstract: This track investigates the relational constitution of data: how stages in the life of data articulate to one another and the challenges involved in storing, moving, classifying, manipulating and interpreting them. The session is intended to explore the collectivities emerging through data collection, dissemination, assemblage and analysis. Analysing the ways in which information becomes taken as given things, the manner in which data and their varying contexts of use are co-constituted, and the means by which utility is invested and divested in them provides a platform to explore and challenge the powers attributed to “Big” and “Open” data by governments, lobby groups and institutions around the world. With its long standing attention to the conditions of knowledge production, STS scholarship is well positioned to reflect on the value(s) attributed to data under a variety of different circumstances, how such attribution changes in time, and what this indicates about the properties of the objects being identified and used as ‘data’ and of the communities involved in such identification and use. Questions to be addressed include: What would it mean to speak of the birth of data? How do they develop, especially when they are used for a variety of purposes by different stakeholders? Do they ever cease to be data, and how can we conceptualize situations in which data are dismissed, forgotten, erased, lost or regarded as obsolete? This session will be organised as a set of individual presentations encompassing several different aspects and areas of data use.

Process: The deadline for submitting an abstract is 21 February 2016. If you want to participate in this open track then you will need to select it when you submit your abstract to the 4S/EASST Conference. Instructions for submission of your abstract are available at http://www.sts2016bcn.org/

. If you would like to discuss the relevance of your paper to the open track, then please contact either or both of us: s.leonelli@exeter.ac.uk

and b.rappert@exeter.ac.uk

social psychological approaches to understanding why people are blasé about data security

I’m reading Untangling the Web, by Aleks Krotoski, as an accessible precursor to beginning to engage with the social psychological literature on online behaviour. It’s proving to be an enjoyable read so far, though maybe not quite as much of a pop social psychology book as I had hoped it would be. It’s more of a collection of thoughtful tech journalism than anything else. But I just came across a good example of what I was initially looking for: different (mutually compatible) social psychological explanations for why people are so blasé about their data. From page 133-134:

There are indeed a few things that are psychologically unique about interacting via machines. First, we don’t expect consequences. The web feels ephemeral, separate from so- called real life. What happens online stays online. That’s totally untrue, of course. As we continue to intertwine our lives with technology, our virtual and physical selves evolve into the same beast, and therefore it’s impossible to separate the consequences that affect one from the other. Something said or done in one place can easily be taken out of context and dropped into another. Ask the many people who’ve been fired from their jobs for posting party pictures on their Facebook timelines.

Second, according to the Ohio study, online we experience an extreme version of the so- called “third person effect”: we rationalise, through our infernal, eternal human nature, that if something’s going to go wrong, it’ll happen to the other guy. So we won’t change our privacy settings on a social network or turn off cookies on our browsers to keep the details of our surfing away from advertisers: only when we experience a personal violation will we be more careful to protect ourselves and our information.

Third, we’re unable to imagine the vastness of the potential audience we communicate with when we’re online, so we treat the computer like a confidant, a confessor. We have an intimate relationship with our computer terminals; our laptops, mobile phones, desktops and tablets feel private, and the networks we hang out in feel closed. In order to make a connection with others, we feel it’s OK to share private information. “We think the web is a kind of conversation,” explains Dr Kieran O’Hara, a philosopher and web scientist at the University of Southampton. “It feels a bit like writing letters, a bit like a telephone conversation. But it’s all that and much more.”

spotify’s ultra-creepy new privacy policy

As the article suggests, this initiative may be the result of the threat posed by Apple music. What interests me is how totally open-ended this is: how do we perceive and evaluate risks when policies take such a form?

Sections 3.3 and 3.4 of Spotify’s privacy policy say that the app will now collected much more data about its users.

The section ‘Information Stored on Your Mobile Device’ says that: “With your permission, we may collect information stored on your mobile device, such as contacts, photos or media files.”

In the next section, it says: “We may also collect information about your location based on, for example, your phone’s GPS location or other forms of locating mobile devices (e.g Bluetooth).”

http://www.independent.co.uk/life-style/gadgets-and-tech/news/spotify-has-announced-a-creepy-new-privacy-policy-and-people-are-worried-10464129.html

data fetishism and the elites of digital capitalism

From Plutocrats: The Rise of the New Global Super-Rich pg 46:

Carlos Slim, who studied engineering in college and taught algebra and linear programming as an undergraduate, attributes his fortune to his facility with numbers. So does Steve Schwarzman, who told me he owed his success to his “ability to see patterns that other people don’t see” in large collections of numbers. People inside the super- elite think the rise of the data geeks is just beginning. Elliot Schrage is a member of the tech aristocracy— he was the communications director for Google when it was the hottest company in the Valley and jumped to the same role at Facebook just as it was becoming a behemoth. At a 2009 talk he gave to an internal company meeting of education and publishing executives, Schrage was asked what field we should encourage our children to study. His instant answer was statistics, because the ability to understand data would be the most powerful skill in the twenty- first century.

How does this intersect with the (purported) rise of the data scientist as the ‘sexist job of the 21st century‘?

The Philosophy of Data Science

Over the last six months I conducted a series of interviews for the LSE Impact Blog about the philosophical challenges which data science poses for the social sciences. Here’s a list of the interviews:

Here are some of my favourite bits from the interviews:

Emma Uprichard: To many the question of what is ‘social’ about the data is not even a necessary question because there seems to be an assumption in some circles that the ‘social bit’ doesn’t really matter; instead what matters are the data. But in the case of big data, the data usually are the social bit! I cannot emphasis this point enough: most big data is *social* data. Yet the modes of analysis applied to big data tend to mirror approaches that have been long been rejected by social scientists. And importantly, they have been rejected not because of the ‘discomfort’ that comes from the idea of modelling human systems using modes of analysis useful to model and think about atoms, fluid dynamics, engine turbulence or social insects etc., although that may be part of it. These kinds of big data methodologies might well be used meaningfully to address certain kinds of questions and ones that we haven’t even asked before. But one of the main reason these ‘social physics’ approaches tend to be rejected by social scientists is that these methodological approaches don’t so much as even a nod to how important social meaning, context, history, culture, notions of agency or structure might be – and yet these matter enormously to how we use data to study social change and continuity.

Emma Uprichard: It is important to appreciate that much of methodological drive to do with big data is coming from commercial entities, which quite rightly are interested in targeting their products and services to most of their clients and customers. However, for social policy and planning purposes, especially around issues to do with social divisions of various kinds, we not only need to know what is mostly happening, we also need to examine the ‘odd cases’, the outliers, the different minority trends, and so on.

Noortje Marres: Digital sociology is a relatively recent term, it has only really come into use a few years ago, which is odd given the fact that sociologists have long played a central role in advancing our understanding of the computerization of social life. Some people propose to define digital sociology primarily in terms of its methods and techniques, saying that it’s centrally concerned with the new opportunities for researching society opened up by digital data. I think this explanation is misguided, as it often ends up simply making the case for a highly particular set of social research techniques, namely statistical methods of data analysis, which are fairly well-established at that. In my view, the recent interest in digital sociology is best understood in terms of changing relations between social life – as an object of research – and social analysis.

Especially crucial is the proliferation of instruments and practices of social analysis across social life in the form of social media platforms, digital analytics, the internet of things, and so on. In this situation, it is not simply the case that new data and new techniques become available for social research. Rather, it means that social actors, practices and events are increasingly and explicitly oriented towards social analysis and are actively involved in it (in collecting and analysing data, applying metrics, eliciting feed-back, and so on). This raises lots of questions about the ways in which technology participates in the representation and doing of ‘social life’, questions which in the past have been considered more of a specialist interest (for the sociology of technology). So digital sociology is a digitally aware form of social inquiry, one which does not seek to bracket the influence of digital technology in the doing of social life and social research. In terms of its relations to other fields, I think that this growing digital awareness in sociology creates lots of opportunities for collaboration between sociology and related fields like data science. Although paradoxically we might find that data scientists believe in society – in the sense of a universe made up of purely human interactions – much more than many sociologists!

Noortje Marres: In the case of digital sociology, the ‘new’ phenemona – monitored living, trackeable sociality, and so on – have been very explicitly associated with what is undeniably a core concern of sociology, namely “the social,” and I think this circumstance carries some promise. Of course, there is the real risk that the type of “sociality” that is studied by digital sociologists, social media scholars, digital anthropologists and so on, will not be recognized as such by other sociologists and social scientist, and seen as not being about ‘the social’ at all, but about something else, like marketing. But I am hoping that the question of what distinguishes different forms and types of sociality, and why these differences matter, is of interest to scholars and researchers across different fields, and so we should seek to formulate this question in the broadest possible terms, while also recognizing that we are all “geeks” to an extent, with particular hang-ups and concerns.

Evelyn Ruppert: We want to change that by providing a platform that doesn’t reduce Big Data to analytics, but extends our understanding it to all practices such as how data is generated and configured as a part of everyday digital practices to how it is curated, categorised, cleaned, accessed, analysed and acted upon. This is a much richer engagement, what you could call a data social science that analyses these practices and at the same time also engages empirically through experimenting with and innovating methods while reflecting on the consequences for how societies are represented (epistemologies), realised (ontologies) and governed (politics).

Evelyn Ruppert: This indeed will be a challenge and while we encourage interdisciplinarity we also recognize that in many cases we will have content that speaks to specific disciplinary approaches that, as you say, talk past each other. Be that as it may, one aspect of the journal that may facilitate such trans-disciplinary conversations and understanding is that its focus is a particular material thing – Big Data – which is a matter of concern across disciplines and about which there is no clear settlement or definition. We are at a moment of discovery, uncertainty and experimentation and it is at such times that I think there is greater openness to different possibilities and ways of thinking. The extent to which we can demonstrate and facilitate this is to be seen. At the same time I think it is important to be modest about what a journal can achieve!

Rob Kitchin: For me, big data has seven traits — they are:

huge in volume, consisting of terrabytes or petabytes of data;

high in velocity, being created in or near real-time;

diverse in variety in type, being structured and unstructured in nature, and often temporally and spatially referenced.

exhaustive in scope, striving to capture entire populations or systems (n=all)

fine-grained in resolution, aiming to be as detailed as possible, and uniquely indexical in identification;

relational in nature, containing common fields that enable the conjoining of different datasets;

flexible, holding the traits of extensionality (can add new fields easily) and scalable (can expand in size rapidly).

Big data then are not simply very large datasets, they have other characteristics. This is why a census dataset does not constitute big data as I define it. True, census data are huge in volume, seeks to be exhaustive, and has high resolution (though they are usually aggregated for release), indexicality and relationality. However, a census has very slow velocity being generated once every ten years, very weak variety consisting of 30 to 40 structured questions, no flexibility or scalability (once formulated a census is set and is being administered it is impossible to tweak or add/remove questions). Another traditionally large dataset, the national household survey, is more timely, usually administered quarterly, but at the sacrifice of exhaustivity (it is sampled), but likewise lacks variety and flexibility.

Rob Kitchin: There are inherent philosophies of data science, whether data scientists recognize this or not. Even if a scientist claims to have no philosophical position, she is expressing a conceptual position about how she makes sense of the world. On questioning, their position with respect to epistemology, ontology, ideology and methodology can be teased out (though it might be slightly confused and not well thought through). Philosophy is important because it provides the intellectual framework that shapes and justifies what kinds of questions are asked, how they are asked, how the answers are made sense of, and what one does with the resulting knowledge. Avoiding it weakens the intellectual rigour of a project and widens the scope of potential critique. Quite often scientists avoid the difficult work of thinking through their philosophical position by simply accepting the tenets of a dominant paradigm, or by operating merely at the level of methodology. Generally, this consists of a claim to using the ‘scientific method’ which tries to position itself as a commonsensical, logical, and objective way to approach understanding the world that is largely beyond question.

As I’ve already discussed, the philosophy of science is not fixed and does change over time with new ideas about how to approach framing and answering questions. This is clearly happening with debates concerning how big data and new forms of data analytics is and can alter the scientific method, and also debates over the approach of the digital humanities and computational social sciences. And even if data scientists do not want to engage in such debates, their work remains nonetheless open to philosophical critique. In my view, the intellectual rigour of data science would be significantly improved by working through its philosophical underpinnings and engaging in debate that would strengthen its position through evolution in thought and practice and which rebuffs and challenges critique. Anything less demonstrates a profound ignorance of the intellectual foundations upon which science is rooted. So, yes, philosophy does and should matter to data science.

Deborah Lupton: We seem unable to talk about the big data phenomenon without using metaphors that are often drawn from nature. The most common metaphorical system that is used employs liquidity metaphors, referring to ‘drowning’ in big data, data ‘flows’ and ‘floods’ or the big data ‘tsunami’. I argue that these metaphors suggest both the sense that big data are constantly mobile and circulating from one site to another, and that they are overwhelming in their magnitude.

Metaphors drawn from nature have been common in representing digital technologies since their emergence, as Sue Thomas points out in her book Technobiophilia. They are a way of seeking to domesticate and familiarise technologies that may appear threatening in their novelty and strangeness, and work to incorporate technologies into a pre-existing world view. Conceptualising digital data as liquid flows helps us to conceptualise and make sense of the phenomenon. But nature is not always benign, as the metaphors of ‘big data floods’ and ‘tsunamis’ suggest. Here again the meaning that there is something threatening about big data emerges.

Deborah Lupton: It may well be the case that established approaches in the social sciences are represented as focusing only on ‘small data’. But I do not see this nomination as a threat, but rather as an opportunity. For a new project I have been investigating portrayals of ‘small’ as compared to ‘big’ data. What is interesting is the value that ‘small data’ are beginning to be invested with many forums. Increasingly we are seeing discussions in the marketing and computer science literature concerning how small data provide meaning to big data and may be more valid. Small data are represented as offering an important alternative to big data because they are viewed as more insightful and detailed and also as more manageable in their size. This relates back to the notion that big data are threatening or challenging because of their volume.

Here again, as critical and reflexive social researchers we can investigate the social and cultural aspects of portrayals of small data in the popular media or technical literature as they are juxtaposed against big data. As we have always done, we can highlight the ways in which small data are socially constructed. We can also continue to generate and use small data for our own research purposes, as we have been doing for a long time. This is where the opportunity to distinguish ourselves as offering a unique perspective on both small and big data emerges.

Susan Halford: Web Science began at the University of Southampton as a label to describe an interdisciplinary space for researchers to explore the evolution of the web as an inextricably sociotechnical set of practices of rapidly increasing significance in shaping the nature of the world around us. The original impetus came from the computational sciences, particularly from those who had been centrally involved in developing the technical architectures of the web in its early years and had come to appreciate that the web was evolving in all kinds of unexpected ways – at once exciting and troublesome –that computer science alone could not address. By drawing in expertise from the social sciences, humanities, medicine & health sciences, law, business and so on – the experiment was to see if and how new forms of critical engagement and analysis might emerge beyond our familiar disciplinary repertoires.

Of course it is perfectly possible to do a sociology of the web, or use the web for sociological research, similarly for anthropology or the digital humanities, but the point of web science is to explore the far wider landscape of questions that emerge from the web as our focus of study – rather than beginning with disciplines and the questions that they might ask about the web. This in itself demands new ways of working & new forms of communication. It took us a while, for instance, to realise that we meant really rather different things by the term ‘ontology’; and disciplinary certainties are continually shattered, for example as joint projects make it apparent to computer scientists that technical capacity might be the least important thing in shaping web use and growth; whilst social scientists began to see the limits of their knowledge about the web as – at least in part – a technical system; and the limits of their methods in apprehending the web and web data.

Susan Halford: The main obstacle is obvious: most sociologists don’t know very much about the infrastructures of the web even in theory, let alone have the skills to get involved in developing web standards and protocols or building web applications. My good colleague Mark Weal (a computer scientist) put it to me, very politely, as follows: sociologists criticise the natural sciences for simplifying the social, failing to capture its diversity and complexity – pouring scorn, for instance on social media analyses that count the number of ‘happy’ or ‘sad’ words in tweets and conclude on the happiness of a nation; but at the same time we – sociologists –have strong tendencies to ‘black box’ the technical, the web for instance, as if treating it all as socially constructed means that no understanding of how it works is necessary.

Of course, you’re right, as well as our disciplinary orientations there is also a question of training. We do not train social scientists in the kinds of techniques and methods that are necessary to engage in depth with the web and, to be honest, I don’t think it is viable to do this to the extent that would be necessary to take us to the level of a computer scientist trained for 8, 10 years or more. It is of course worth starting some basic training – we do this on our MSc in Web Science, where the programme that we designed led in particular by Catherine Pope and Les Carr at Southampton ensures that all students – regardless of background – take modules in semantic web technologies and hypertext (more challenging for the theologists and sociologists) as well as in social theory (which challenges the computer scientists and mathematicians). This produces informed researchers able to engage across disciplines, which is important, and develop interdisciplinary PhD projects. But ultimately we still need to draw on deep expertise that has, to date, been grounded in particular disciplines, as I argued above.

Sabina Leonelli: the current manifestations of data-centric science have distinctive features that relate to the technologies, institutions and governance structures of the contemporary scientific world. For instance, this approach is typically associated to the emergence of large-scale, multi-national networks of scientists; to a strong emphasis on the importance of sharing data and regarding them as valuable research outputs in and of themselves, regardless of whether or not they have yet been used as evidence for a given discovery; the institutionalization of procedures and norms for data dissemination through the Open Science and Open Data movements, and policies such as those recently adopted by RCUK and key research funders such as the European Research Council, the Wellcome Trust and the Gates Foundation; and the development of instruments, building on digital technologies and web services, that facilitate the production and dissemination of data with a speed and geographical reach as yet unseen in the history of science. In my work, I stress how this peculiar conjuncture of institutional, socio-political, economic and technological developments have made data-centric science into a prominent research approach, which has considerably increased international debate and active reflection over processes of data production, dissemination and interpretation within science and beyond. This level of reflexivity over data practices is what I regard as the most novel and interesting aspect of contemporary data-centrism.

Sabina Leonelli: The epistemological aspect that interests me most, however, is even more fundamental. Given the central role of data in making scientific research into a distinctive, legitimate and non-dogmatic source of knowledge, I view the study of data-intensive science as offering the opportunity to raise foundational questions about the nature of knowledge and knowledge-making activities and interventions. Scientific research is often presented as the most systematic set of efforts in the contemporary world aimed to critically explore and debate what constitutes acceptable and sufficient evidence for any given belief about reality. The very term ‘data’ comes from the Latin ‘givens’, and indeed data are meant to document as faithfully and objectively as possible whatever entities or processes are being investigated. And yet, data collection is always steeped in a specific way of understanding the world and constrained by given material and social conditions, and the resulting data are therefore marked by the historical circumstances through which they were generated: what constitutes trustworthy or sufficient data changes across time and space, making it impossible to ever assemble a complete and intrinsically reliable dataset. Furthermore, data are valued and used for a variety of reasons within research, including as sources of evidence, tokens of exchange and personal identity, signifiers of status and markers of intellectual property; and myriads of data types are produced by as many stakeholders, from citizens to industry and governmental agencies, which means that what constitutes data, for whom and for which purposes is constantly at stake.

This landscape makes the study of data into an excellent entry point to reflect on the activities and claims associated to the idea of scientific knowledge, and the implications of existing conceptualisations of various forms of knowledge production and use. This is nicely exemplified by an ongoing Leverhulme Trust Research Grant on the digital divide in data handling practices across developed and developing countries, particularly sub-Saharan Africa, which we are currently developing at Exeter – what constitutes knowledge, and a ‘scientific contribution’, varies enormously depending not only on access to data, but also on what is regarded as relevant data in the first place, and what capabilities any research group has to develop, structure and disseminate their ideas.

Sociological questions about the coming era of data-driven privatised policing

This insightful article paints a worrying picture of the growth of data-driven policing. The technical challenge of “building nuance” into data systems “is far harder than it seems” and has important practical implications for how interventions operate on the basis of digital data. What I hadn’t previously realised was how readily investigators are using social media on their own initiative above and beyond the systems that are being put into place with the help of outside consultancies: only 9% of police using social media in investigations had received training from their agency. Furthermore the discussion of the life span of data raised some really interesting (and worrying) questions about the organisational sociology of data-driven policing given what seems likely to be increasing involvement of the private sector in policing in the UK:

For the kid listed in a gang database, it can be unclear how to get out of it. In the world of human interaction, we accept change through behavior: the addict can redeem himself by getting clean, or the habitual interrupter can redeem himself by not interrupting. We accept behavior change. But in the database world, unless someone has permission to delete or amend a database record, no such change is possible. Credit agencies are required to forgive financial sins after 7 years. Police are not—at least, not consistently. The National Gang Center, in its list of gang-related legislation, shows only 12 states with policies that specifically address gang databases. Most deny the public access to the information in these databases. Only a few of these twelve mention regular purging of information, and some specifically say that a person cannot even find out if they have a record in the database.

This permanence does not necessarily match real-world conditions. Kids cycle in and out of street gangs the way they cycle in and out of any other social group, and many young men age out of violent behavior. Regularly purging the gang database, perhaps on a one-year or two-year cycle, would allow some measure of computational forgiveness. However, few institutions are good at keeping the data in their databases up-to-date. (If you’ve ever been served an ad for a product you just bought, you’re familiar with this problem of information persistence and the clumsiness of predictive algorithms.) The police are no worse and no better than the rest of us. Criminologist Charles Katz found that despite a written department policy in one large Midwestern police gang unit, data was not regularly audited or purged. “The last time that the gang unit purged its files, however, was in 1993—approximately 4 years before this study was conducted,” he wrote. “One clerk who is responsible for data entry and dissemination estimated, ‘At a minimum, 400 to 500 gang members would be deleted off the gang list today if we went through the files.’ Accordingly, Junction City’s gang list of 2,086 gang members was inflated by approximately 20% to 25%.”

http://www.theatlantic.com/politics/archive/2015/04/when-cops-check-facebook/390882/

This suggests to me that any adequate evaluation of data-driven policing needs to take questions of organisational sociology and information technology extremely seriously. What matters is not just the formulation of data management policies but what we know about how such policies tend to be implemented under the specific conditions likely to obtain in policing. Given the broader trend towards the privatisation of policing, it is increasingly important that we understand how sharing of data operates across organisational boundaries, how it is prepared and how it is perceived by end-users.

My fear is that a form of inter-organisational ‘black-boxing’ could kick in where those utilising the data for interventions trust that others have elsewhere taken responsibility for ensuring its reliability. What scrutiny would the operations of outside suppliers be subject to? Could privatisation intensify the rush towards data-driven policing in the name of efficiency savings? Would a corresponding centralisation of back-office functions compound the aforementioned epistemological risks entailed by outsourcing? These are all urgent questions which could easily be marginalised as budgetary constraint drives ‘innovation’ in policing: data-driven policing and privatised policing will likely go hand-in-hand and we need to analyse them as such.

CfP: The Politics of Data (Science)

The Politics of Data (Science)

This special issue of Discover Society will explore the political implications of ‘big data’ and the systems of expertise emerging around it, including though not limited to Data Science. In doing so it will aim to bridge the gap between the methodological discourse surrounding data science and the political discourse beginning to emerge around ‘big data’. Here are some of the questions the issue will address:

– How is ‘big data’ understood and acted upon? How should we understand its cultural power?
– How is ‘big data’ reconfiguring the social sciences? Do we risk all science becoming data science?
– How and why has the ‘data scientist’ come to be seen as the ‘sexiest job of the 21st century’?
– Is the ‘data scientist’ just a ’Statistician who lives in Shoreditch?’ Or is this a genuinely new intellectual role?
– Can ‘big data’ address ‘big questions’? If not, is this a problem?
– What are the precursors of ‘data science’ within the academy and/or within corporations?
– What implications does corporate data science have for the relationship between corporations & consumers?
– What implications does national security data science have for the relationship between the state & citizens?
– Can the use of digital data lead to efficiency savings in public services? How does this relate to the politics of austerity?
– How could predictive privacy harms emerging from data analytics be addressed politically?
– Can the opacity of algorithmic processes be challenged? Or are we heading inexorably for a ‘black-box society’?
– How are new forms of digital data reconfiguring activity in particular social environments?

However these are just suggestions and ideas beyond the scope of this list are very welcome.

The deadline for contributions is June 15th. Contact mark@markcarrigan.net to discuss a potential contribution.

The article will constitute the July issue of Discover Society. Most articles will be 1500 words however there are a number of special sections in the online magazine.

Front line – 1500 words
View point – 1500 words
Policy briefing – 1500-2000 words

If you would be interested in writing one of these thematic sections, please get in touch asap.

The issue will follow the usual formatting guidelines of Discover Society. Please consult the notes for contributors.

Call for papers: Internet of You: Data Big and Small

This looks really interesting – if I wasn’t drowning under the weight of existing writing commitments, I’d love to try and write something for the final topic suggestion:

Call for papers for special issue of IEEE Internet Computing – http://www.computer.org/portal/web/computingnow/iccfp6

Internet of You: Data Big and Small

Final submissions due: 1 March 2015
Publication issue: November/December 2015

Please email the guest editors a brief description of the article you plan to submit by 1 February 2015.
Guest editors: Deborah Estrin and Craig Thompson (ic6-2015@computer.org).

We are at a great divide. Where our ancestors left behind few records, we are creating and preserving increasingly complete digital traces and models of almost every aspect of our lives. This special issue of IEEE Internet Computing aims to explore technologies and issues from small user-centric models of individuals to real-time analytics on huge aggregations of user data. At present, some are aspiring to create immortal avatars by letting you record everything about yourself and convert it into a model that’s queriable, conversant, and possibly even active in gaining new experiences for itself. Meanwhile, others are equally concerned with stemming the tide of third-party data aggregation of individual models to mitigate risks that can evolve from this kind of near total information awareness.

This special issue seeks original articles that explore both small data (individual-scale data sources, processing, and modeling) and big data (community level aggregation and analytics). Topics include

diverse data sources and digital traces, including email, Facebook, financial, health, location, images, sound, consumer transactions, and interests;

methods to combine trace data into complete models; data liberation; kinds of user models, such as the physical self, memories, aspect-limited versus comprehensive models; and data quality, including managing history, change, comprehensiveness, and accuracy;

methods to aggregate and process heterogeneous data sets, stages of life ontologies, the scope and purpose of these data collections, available public data sources;

usage models for experience sampling — proximity, context, activity sensing, quantified self, situation-aware modeling, activities of daily living, my immortal avatar, workflows, and pattern learning;

representation technologies such as agents, smartphones, wearable computing, personal sensing networks, pattern representation and adaptation, and natural language;

new kinds of applications that draw insights from data analytics— including, recommendation systems, personalized health, real-time marketing, and predicting elections from twitter feeds;

open architectures for personalization, the role of cloud computing, relevant emerging standards;

concerns regarding privacy and surveillance, the extent of privacy erosion, taxonomy of privacy threats, and incentives and disincentives for sharing, the right to forget, and status of legal safeguards;

privacy and security technology safeguards, including identity management, disclosure control, privacy-preserving data mining, de-identification, new security models, mechanisms that audit and control personal information flows and usage; and

social and philosophical implications for humans’ conception of self. Is there a natural boundary between user models and world models?

Submission Guidelines

All submissions must be original manuscripts of fewer than 5,000 words, focused on Internet technologies and implementations. All manuscripts are subject to peer review on both technical merit and relevance to IC’s international readership — primarily practicing engineers and academics who are looking for material that introduces new technology and broadens familiarity with current topics. We do not accept white papers, and we discourage strictly theoretical or mathematical papers. To submit a manuscript, please log on to ScholarOne (https://mc.manuscriptcentral.com:443/ic-cs) to create or access an account, which you can use to log on to IC’s Author Center (http://www.computer.org/portal/web/peerreviewmagazines/acinternet) and upload your submission.

“When I first read Foucault’s account of the panopticon. I thought it was brilliant but overheated. Now, it actually seems like somebody’s plan”

An absolute must read essay in the Guardian by the novelist John Lanchester who was given access to the GCHQ files by the Guardian:

The totalitarian state in Orwell’s Nineteen Eighty-Four would need no broader legal justification than that: it really does allow a government to do anything it likes. It was at this point that I became convinced that Snowden’s revelations are not just interesting or important but vital, because the state is about to get powers that no state has ever had, and we need to have a public debate about those powers and what their limits are to be.

At a moment of austerity and with a general sense that our state’s ability to guarantee prosperity for its citizens is in retreat, that same state is about to make the biggest advance ever in its security powers. In public, the state is shrinking; in private, it is shrinking until it gets just small enough to fit into our phones, our computers, our cars, our fridges, our bedrooms, our thoughts and intentions.

[…]

People misunderstand what a police state is. It isn’t a country where the police strut around in jackboots; it’s a country where the police can do anything they like. Similarly, a security state is one in which the security establishment can do anything it likes.

We are right on the verge of being an entirely new kind of human society, one involving an unprecedented penetration by the state into areas which have always been regarded as private. Do we agree to that? If we don’t, this is the last chance to stop it happening. Our rulers will say what all rulers everywhere have always said: that their intentions are good, and we can trust them. They want that to be a sufficient guarantee.

http://www.theguardian.com/world/2013/oct/03/edward-snowden-files-john-lanchester

Mark Carrigan

Tag: data