Lively devices, lively data and lively leisure studies

This is a foreword I wrote for a Leisure Studies special issue on digital leisure cultures (the link to the journal version is here).

In the countries of the Global North, each person, to a greater or lesser degree, has become configured as a data subject. When we use search engines, smartphones and other digital devices, apps and social media platforms, and when we move around in spaces carrying devices the record our geolocation or where there are embedded sensors or cameras recording our movements, we are datafied: rendered into assemblages of digital data. These personal digital data assemblages are only ever partial portraits of us and are constantly changing: but they are beginning to have significant impacts on the ways in which people understand themselves and others and on their life opportunities and chances. Leisure cultures and practices are imbricated within digital and data practices and assemblages. Indeed, digital technologies are beginning to transform many areas of life into leisure pursuits in unprecedented ways, expanding the purview of leisure studies.

These processes of datafication can begin even before birth and continue after death. Proud expectant parents commonly announce pregnancies on social media, uploading ultrasound images of their foetuses and sometimes even creating accounts in the name of the unborn so that they can ostensibly communicate from within the womb. Images from the birth of the child may also become publicly disseminated: as in the genre of the childbirth video on YouTube. This is followed by the opportunity for parents to record and broadcast many images of their babies’ and children’s lives. At the other end of life, many images of the dying and dead bodies can now be found on the internet. People with terminal illnesses write blogs, use Facebook status updates or tweet about their experiences and post images of themselves as their bodies deteriorate. Memorial websites or dedicated pages on social media sites are used after people’s death to commemorate them. Beyond these types of datafication, the data generated from other interactions online and by digital sensors in devices and physical environments constantly work to generate streams of digital data about people. In some cases, people may choose to generate these data; in most other cases, they are collected and used by others, often without people’s knowledge or consent. These data have become highly valuable as elements of the global knowledge economy, whether aggregated and used as big data sets or used to reveal insights into individuals’ habits, behaviours and preferences.

One of my current research interests is exploring the ways in which digital technologies work to generate personal information about people and how individuals themselves and a range of other actors and agencies use these data. I have developed the concept of ‘lively data’, which is an attempt to incorporate the various elements of how we are living with and by our data. Lively data are generated by lively devices: those smartphones, tablet computers, wearable devices and embedded sensors that we live with and alongside, our companions throughout our waking days. Lively data about humans are vital in four main respects: 1) they are about human life itself; 2) they have their own social lives as they circulate and combine and recombine in the digital data economy; 3) they are beginning to affect people’s lives, limiting or promoting life chances and opportunities (for example, whether people are offered employment or credit); and 4) they contribute to livelihoods (as part of their economic and managerial value).

These elements of datafication and lively data have major implications for leisure cultures. Research into people’s use of digital technologies for recreation, including the articles collected here and others previously published in this journal, draws attention to the pleasures, excitements and playful dimensions of digital encounters. These are important aspects to consider, particularly when much research into digital society focuses on the limitations or dangers of digital technology use such as the possibilities of various types of ‘addiction’ to their use or the potential for oppressive surveillance or exploitation of users that these technologies present. What is often lost in such discussions is an acknowledgement of the value that digital technologies can offer ordinary users (and not just the internet empires that profit from them). Perspectives that can balance awareness of both the benefits and possible drawbacks of digital technologies provide a richer analysis of their affordances and social impact. When people are using digital technologies for leisure purposes, they are largely doing so voluntarily: because they have identified a personal use for the technologies that will provide enjoyment, relaxation or some other form of escape from the workaday world. What is particularly intriguing, at least from my perspective in my interest in lively data, is how the data streams from digitised leisure pursuits are becoming increasingly entangled with other areas of life and concepts of selfhood. Gamification and ludification strategies, in which elements of play are introduced into domains such as the workplace, healthcare, intimate relationships and educational institutions, are central to this expansion.

Thus, for example, we now see concepts of the ‘healthy, productive worker’, in which employers seek to encourage their workers to engage in fitness pursuits to develop highly-achieving and healthy employees who can avoid taking time out because of illness and operate at maximum efficiency in the workplace. Fitness tracker companies offer employers discounted wearable devices for their employees so that corporate ‘wellness’ programs can be put in place in which fitness data sharing and competition are encouraged among employees. Dating apps like Tinder encourage users to think of the search for partners as a game and the attractive presentation of the self as a key element in ‘winning’ the interest of many potential dates. The #fitspo and #fitspiration hashtags used in Instagram and other social media platforms draw attention to female and male bodies that are slim, physically fit and well-groomed, performing dominant notions of sexual attractiveness. Pregnancy has become ludified with a range of digital technologies. Using their smartphones and dedicated apps, pregnant women can take ‘belfies’, or belly selfies, and generate time-lapse videos for their own and others’ entertainment (including uploading the videos on social media sites). 3D-printing companies offer parents the opportunity to generate replicas of their foetuses from 3D ultrasounds, for use as display objects on mantelpieces or work desks. Little girls are offered apps which encourage then to perform makeovers on pregnant women or help them deliver their babies via caesarean section. In the education sector, digitised gamification blurs leisure, learning and physical fitness. Schools are beginning to distribute heart rate monitors, coaching apps and other self-tracking devices to children during sporting activities and physical education classes, promoting a culture of self-surveillance via digital data at the same time as teachers’ monitoring of their students’ bodies is intensified. Online education platforms for children like Mathletics encourage users to complete tasks to win medals and work their way up the leaderboard, competing against other users around the world.

In these domains and many others, the intersections of work, play, health, fitness, education, parenthood, intimacy, productivity, achievement and concepts of embodiment, selfhood and social relations are blurred, complicated and far-reaching. These practices raise many questions for researchers interested in digitised leisure cultures across the age span. What are the affordances of the devices, software and platforms that people use for leisure? How do these technologies promote and limit leisure activities? How are people’s data used by other actors and agencies and in what ways do these third parties profit from them? What do people know about how their personal details are generated, stored and used by other actors and agencies? How do they engage with their own data or those about others in their lives? What benefits, pleasures and opportunities do such activities offer, and what are their drawbacks, risks and harms? How are the carers and teachers of children and young people encouraging or enjoining them to use these technologies and to what extent are they are aware of the possible harms as well as benefits? How are data privacy and security issues recognised and managed, on the part both of those who take up these pursuits voluntarily and those who encourage or impose them on others? When does digitised leisure begin to feel more like work and vice versa: and what are the implications of this?

These questions return to the issue of lively data, and how these data are generated and managed, the impact they have on people’s lives and concepts of selfhood and embodiment. As I noted earlier, digital technologies contribute to new ways of reconceptualising areas of life as games or as leisure pursuits that previously were not thought of or treated in those terms. In the context of this move towards rendering practices and phenomena as recreational and the rapidly-changing sociomaterial environment, all social researchers interested in digital society need to be lively in response to lively devices and lively data. As the editors of this special issue contend, researching digital leisure cultures demands a multidisciplinary and interdisciplinary perspective. Several exciting new interdisciplinary areas have emerged in response to the increasingly digitised world: among them internet studies, platform studies, software studies, critical algorithm studies and critical data studies. The ways in which leisure studies can engage with these, as well the work carried out in sub-disciplines such as digital sociology, digital humanities and digital anthropology, have yet to be fully realised. In return, the key focus areas of leisure studies, both conceptually and empirically – aspects of pleasure, performance, politics and power relations, embodiment, selfhood, social relations and the intersections between leisure and work – offer much to these other areas of enquiry.

The articles published in this special issue go some way to addressing these issues, particularly in relation to young people. The contributors demonstrate how people may accept and take up the dominant assumptions and concepts about idealised selves and bodies expressed in digital technologies but also how users may resist these assumptions or seek to re-invent them. As such, this special issue represents a major step forward in promoting a focus on the digital in leisure studies, working towards generating a lively leisure studies that can make sense of the constantly changing worlds of lively devices and lively data.

Living Digital Data research program

People’s encounters and entanglements with the personal digital data that they generate is a new and compelling area of research interest in this age of the ascendancy of digital data. Members of the public are now called upon to engage with a variety of forms of information about themselves and to confront the complexities of how these details are used by others. Personal digital data assemblages are configured as human bodies, digital devices, code, data, time and space come together.

 

Personal digital data assemblages

 

Over the past few years I have been researching the social aspects of personal digital data: how people understand and conceptualise these data, how they use their data, what people know about where their personal data go and how their data are used by second and third parties.I have analysed the metaphors that are used to describe digital data, the politics of digital data, the types of data that are collected by apps and self-tracking devices, how people use these software and devices and how personal digital data are materialised, or rendered into visualisations or three-dimensional objects. I have sought to theorise the ontology of personal digital data, drawing particularly sociomaterialism, feminist technoscience, cultural geography and sensory studies. (See My Recent Publications for further details.)

I am bringing these research questions together under a program that I have named ‘Living Digital Data’. This title builds on my conceptualisation of digital data as ‘lively’ in a number of ways.

 

 

Lively Data

 

 

The first element of the vitality of digital data relates to the ways in which they are generated and what happens thereafter. The personal digital information that is constantly generated contributes to data assemblages that are heterogeneous and dynamic, their character changing as more data points are added and others removed. Digital data may be described as having their own social lives as they circulate in the digital data economy and are purposed and repurposed. Second, digital data constitute forms of knowledge about human (and nonhuman) life itself and hence possess another type of vitality. Third, personal digital data have impacts on people’s lives, shaping the decisions and actions that people make and those that other people make about them.  The profiles constructed from these data can influence decisions about the opportunities people have to travel, access employment, credit or insurance, the people that they meet on online dating sites, the knowledges that they hold about themselves and their bodies and those of intimate others. Finally, personal digital data are forms of livelihoods, contributing to the commodification and capitalisation of information. Indeed, they may be described as a form of biocapital, which possesses many forms of value beyond the personal: for research, commercial, security, managerial and governmental agencies.

This approach recognises the entanglements of personal digital data assemblages with human action. Not only are personal digital data assemblages partly comprised of information about human action, but their materialisations are also the products of human action, and these materialisations can influence future human action.

Rather than refer to data literacy or data management skills, I take up the term ‘data sense’ to encapsulate a broader meaning of ‘data sense’ that includes human senses (sight, sound, touch, smell and taste) and how these are part of people’s responses to data and also acknowledges the role played by digital sensors in the act of ‘sense-making’; or coming to terms with digital data.

 

Data Sense

 

Public understanding of personal digital data

One of the research projects I am conducting, with Mike Michael from the University of Sydney, is investigating the public understanding of digital data. We are experimenting with using novel methods (for sociologists at least!) in our project, which combines focus group discussions with cultural probes.

In the discussion groups, we wanted to go beyond the usual approach of simply asking questions of people. We wanted to invite people to think and work together playfully and creatively. We therefore decided to employ cultural probes to stimulate thought, discussion and debate, involving asking people to work together as a group or in pairs to generate material artefacts. Cultural probes are objects or tasks that are designed to be playful and provocative so as to encourage people to think in new ways about technologies. They are particularly valued for their ability to address intimate or controversial issues, to act as ‘irritants’ to engage people’s responses.

We asked people in our focus groups (held in Sydney) to engage in three collaborative tasks that we devised for them.

1. The Daily Big Data Task. This task asked participants to work together as a group to draw a timeline on a huge piece of paper of a typical person’s day and adding the ways in which data (digital or otherwise) may be collected on that person.
2. The Digital Profile Card Game. This task involved small groups to use cards with socio-demographic details on them to construct a profile of an individual, speculate about their characteristics and discuss how this information could be used.
3. The Personal Data Machine. In this activity, participants were asked to work in pairs to design two data-gathering devices: one that they would find useful to use to collect any kind of data about themselves, and one for collecting data on another person. They were asked to write notes or make drawings describing their devices.

After each task the group came together to talk about the artefacts they had created or handled and what their implications were.

Earlier this year, we published a short piece in Discover Society that outlined some of our initial findings. A new article in the journal Public Understanding of Science, entitled ‘Toward a manifesto for the public understanding of big data’ has just been published. We are currently working on another article that presents our empirical work in greater detail.

In the ‘manifesto’ article, we pointed out that there are many intersections between research on the public understanding of digital data with the literatures on the public understanding of science and public engagement with science and technology. These are bodies of work that have been devoted to making sense of the intersections between how citizens engage with scientific knowledge, including not only consuming but producing this knowledge. Indeed, it may be contended that members of the public have been ‘prosumers’ of scientific knowledge long before the emergence of digital data (that is, both acting to consume and produce scientific information), particularly when they are engaging in citizen activism or citizen science initiatives.

There are many examples of citizens participating in activities that either contribute to or challenge accepted scientific beliefs. This critical approach has led to the development of a public engagement with science and technology model, in which ‘the public’ and ‘scientific expertise’ are not contrasted with each other. Rather, it is acknowledged that each draws their definitions from the other, contributing to hybrid assemblages of knowledges. The public are both the subjects and objects of scientific research and data, just as they are of digital data.

Our research project findings suggest that we may be seeing a transformation in attitudes in response to the controversies and scandals in relation to the use of people’s personal data that have received a high level of public attention over the past two and a half years, potentially reshaping concepts of privacy. What emerged from our focus groups is a somewhat diffuse but quite extensive understanding on the part of the participants of the ways in which data may be gathered about them and the uses to which these data may be put.

It was evident that although many participants were aware of these issues, they were rather uncertain about the specific details of how their personal data became part of big data sets and for what this information was used. While the term ‘scary’ was employed by several people when describing the extent of data collection in which they are implicated and the knowledge that other people may have about them from their online interactions and transactions, they struggled to articulate more specifically what the implications of such collection were.

On the other hand, when the participants were designing their ‘Personal Data Machines’, it was evident from their creations that they appreciated and enjoyed the opportunity not only to collect detailed information about themselves, but also on other people: partners, children and other family members and co-workers. Some imagined devices included those that monitored other people’s dreams or snooped on partners’ phone call metadata to check if they were unfaithful. Other people described a lie-detecting device, one that could track commercial competitors’ activities, another that revealed the salary of their workmates (so that the user could know if they were being fairly remunerated) and a dating device that could scan a prospective partner’s hand or face and reveal their financial assets and criminal record details.

In some cases, it seems, too much information is never enough. The seductions of data can be very appealing, not just for commercial enterprises or national security agencies.

 

Who owns your personal health and medical data?

Presenting my talk at the Mayo Clinic Social Media and Healthcare Summit (Photo by Jason Pratt / Mayo Clinic)

Tomorrow I am speaking on a panel at the Mayo Clinic Healthcare and Social Media Summit on the topic of ‘Who owns your big data?’. I am the only academic among the panel members, who comprise of a former president of the Australian Medical Association, the CEO of the Consumers Health Forum, the Executive Director of a private hospital organisation and the Chief Executive of the Medical Technology Association of Australia. The Summit itself is directed at healthcare providers, seeking to demonstrate how they may use social media to publicise their organisations and promote health among their clients.

As a sociologist, my perspective on the use of social media in healthcare is inevitably directed at troubling the taken-for-granted assumptions that underpin the jargon of ‘disruption’, ‘catalysing’, ‘leveraging’ and ‘acceleration’ that tend to recur in digital health discourses and practices. When I discuss the big data phenomenon, I evoke the ‘13 Ps of big data‘ which recognise their social and cultural assumptions and uses.

When I speak at the Summit, I will note that the first issue to consider is for whom and by whom personal health and medical data are collected. Who decides whether personal digital data should be generated and collected? Who has control over these decisions? What are the power relations and differentials that are involved? This often very intimate information is generated in many different ways – via routine online transactions (e.g. Googling medical symptoms, purchasing products on websites) or more deliberately as part of people’s contributions to social media platforms (such as PatientsLikeMe or Facebook patient support pages) or as part of self-tracking or patient self-care endeavours or workplace wellness programs. The extent to which the generation of such information is voluntary, pushed, coerced or exploited, or indeed, even covert, conducted without the individual’s knowledge or consent, varies in each case. Many self-trackers collect biometric data on themselves for their private purposes. In contrast, patients who are sent home with self-care regimes may do so reluctantly. In some situations, very little choice is offered people: such as school students who are told to wearing self-tracking devices during physical education lessons or employees who work in a culture in which monitoring their health and fitness is expected of them or who may be confronted with financial penalties if they refuse.

Then we need to think about what happens to personal digital data once they are generated. Jotting down details of one’s health in a paper journal or sharing information with a doctor that is maintained in a folder in a filing cabinet in the doctor’s surgery can be kept private and secure. In this era of using digital tools to generate and archive such information, this privacy and security can no longer be guaranteed. Once any kind of personal data are collected and transmitted to the computing cloud, the person who generated the data loses control of it. These details become big data, part of the digital data economy and available to any number of second or third parties for repurposing: data mining companies, marketers, health insurance, healthcare and medical device companies, hackers, researchers, the internet empires themselves and even national security agencies, as Edward Snowden’s revelations demonstrated.

Even the large institutions that are trusted by patients for offering reliable and credible health and medical information online (such as the Mayo Clinic itself, which ranks among the top most popular health websites with 30 million unique estimated monthly visitors) may inadvertently supply personal details of those who use their websites to third parties. One recent study found that nine out of ten visits to health or medical websites result in data being leaked to third parties, including companies such as Google and Facebook, online advertisers and data brokers because the websites use third party analytic tools that automatically send information to the developers about what pages people are visiting. This information can then be used to construct risk profiles on users that may shut them out of insurance, credit or job opportunities. Data security breaches are common in healthcare organisations, and cyber criminals are very interested in stealing personal medical details from such organisations’ archives. This information is valuable as it can be sold for profit or used to create fake IDs to purchase medical equipment or drugs or fraudulent health insurance claims.

In short, the answer to the question ‘Who owns your personal health and medical data?’ is generally no longer individuals themselves.

My research and that of others who are investigating people’s responses to big data and the scandals that have erupted around data security and privacy are finding that concepts of privacy and notions of data ownership are beginning to change in response. People are becoming aware of how their personal data may be accessed, legally or illegally, by a plethora of actors and agencies and exploited for commercial profit. Major digital entrepreneurs, such as Apple CEO Tim Cook, are in turn responding to the public’s concern about the privacy and security of their personal information. Healthcare organisations and medical providers need to recognise these concerns and manage their data collection initiatives ethically, openly and responsibly.

‘Eating’ digital data

Update: I have now published a journal article that brings this blog with the previous one and expands the argument – it can be found here.

My previous post drew on Donna Haraway’s concept of companion species to theorise the ways in which we engage with our personal digital data assemblages. The work of Annemarie Mol offers an additional conceptual framework within which to understand digital data practices at a more detailed level, while still retaining the companion species perspective.

Mol has developed a framework that incorporates elements of enquiry that can be mapped onto the topic of digital data practices. These include the following: understanding language/discourse and its context and effects; tracing the development and use of objects of knowledge as they become objects-in-practice; acknowledging the dynamic nature of processes and the ‘endless tinkering’ that is involved in processes; incorporating awareness of the topologies or sites and spaces in which phenomena are generated and used; and finally, directing attention at the lived experiences or engagements in which practices and objects are understood and employed (see also Mol, 2002, 2008; Mol and Law, 2004).

If this approach is applied to digital data practices and their configurations, then focusing attention on the language that is employed to describe digital data, viewing digital data as objects that have both discursive and material effects and that constantly changing, recognising the process of tinkering (experimenting, adapting) that occur in relation to digital data and the spaces in which these processes take place are all important to developing an understanding of the ontology of digital data and our relationship with them.

Mol’s (2002) concept of ‘the body multiple’ in medicine has resonances with the Haraway’s cyborg ontology. This concept recognises that the human body is comprised of many different practices, sites and knowledges. While the body itself is not fragmented or multiple, the phenomena that make sense of it and represent it do so in many different ways so that the body is lived and experienced in different modes. So too the digital data assemblages that are configured by human users’ interactions with digital technologies are different versions of people’s identities and bodies that have material effects on their ways of living and conceptualising themselves. Part of the work of people’s data practices is negotiating the multiple bodies and selves that these digital data assemblages represent and configure.

Mol’s writings on human subjectivity also have implications for understanding data practices and interpretations. In her essay entitled ‘I eat an apple’, Mol points out that once a foodstuff has been swallowed, the human subject loses control over what happens to the content of the food in her body as the processes of digestion take place. As she notes, the body is busily responding to the food, but the individual herself has no control over this: ‘Her actorship is distributed and her boundaries are neither firm nor fixed’ (Mol, 2008: 40). The eating subject is able to choose what food she decides to eat, but after this point, her body decides how to deal with the components of the food, selecting certain elements and discarding others.

This raises questions about human agency and subjectivity. In the statement ‘I eat an apple’ is the agency in the ‘I’ or in the apple? Humans may grow, harvest and eat apples, but without foodstuffs such as apples, humans would not exist. Furthermore, once the apple is chewed and swallowed, it then becomes part of and absorbed into the eater’s body. It is impossible to determine what is human and what is apple (Mol, 2008: 30) The eating subject, therefore, is semi-permeable, neither completely closed off nor completely open to the world.

Mol then goes on to query at what stage the apple becomes part of her, and whether the category of the human subject might recognise the apple as ‘yet another me, a subject in its own right’ (Mol, 2008: 40). Apples themselves have been shaped by years of cultivation by humans into the forms in which they now exist. In fact they may be viewed as a form of Haraway’s companion species. How then do we draw boundaries around the body/self and the apple? How is the human subject to be defined?

To extend Mol’s analogy, the human subject may be conceptualised as both data-ingesting and data-emitting in an endless cycle of generating data, bringing the data into the self, generating yet more data. Data are absorbed into the body/self and then become new data that flow out of the body/self into the digital data economy. The data-eating/emitting subject, therefore, is not closed off but is open to taking in and letting out digital data. These data become part of the human subject but, as data assemblages also represent the individual in multiple ways that have different meanings based on their contexts and uses. Just as eating an apple has many meanings, depending on the social, cultural, political, historical and geographical contexts in which this act takes place, generating and responding to digital data about oneself are highly contingent acts. If digital data are never ‘raw’ but rather are always ‘cooked’ (that is, always understood and experienced via social and cultural processes), and may indeed be ‘rotted’ or spoilt in some way (Boellstorff, 2013), can we also understand them as ‘eaten’ and ‘digested’?

Haraway and Mol both emphasise the politics of technocultures. Haraway’s cyborg theorising was developed to explain her socialist feminist principles. In all of her work she emphasises the importance of paying attention as critical scholars to the exacerbation of socioeconomic disadvantage and inequalities that may be outcomes of these relationships. Mol similarly notes the political nature of technologies. In her ‘I eat an apple’ essay, for example, she comments about her distaste for Granny Smith apples, once imported from Chile and therefore associated in her mind with repressive political regimes. As she notes, while she may eat this type of apple and while it may nourish her body as other apples do, she is unable to gain sensory pleasure from it.

Data science writings on big data often fail to acknowledge the political dimensions of digital data. They do not see how data are always already ‘cooked’, or how their flavour or digestibility are influenced by their context. Just as ‘eating apples is variously situated’ (Mol, 2008: 29) in history, geography, culture, social relations and politics, resulting in different flavours and pleasures, so too eating data is contextual. Like Haraway’s cyborg figuration (see her interview with Gane, 2006), the digital data assemblage may be viewed both as a product of global enterprise and capitalism and as representing possibilities for radical creative and political possibilities.

Using Mol’s concepts of the eating subject, we might wonder: What happens when we ingest/absorb digital data about ourselves? Do we recognise some data as ‘food’ (appropriate for such ingestion) and others as ‘non-food’ (not appropriate in some way for our use)? Are some data simply indigestible (our bodies/selves do not recognise them as us and cannot incorporate them)? How are the flavours and tastes of digital data experienced, and what differentiates these flavours and tastes?

References

Boellstorff, T. (2013) Making big data, in theory. First Monday, 18 (10). <http://firstmonday.org/ojs/index.php/fm/article/view/4869/3750&gt;, accessed 8 October 2013.

Gane, N. (2006) When we have never been human, what is to be done?: Interview with Donna Haraway. Theory, Culture & Society, 23, 135-58.

Mol, A. (2002) The Body Multiple: Ontology in Medical Practice. Durham, NC: Duke University Press.

Mol, A. (2008) I eat an apple. On theorizing subjectivities. Subjectivity, 22, 28-37.

Mol, A. & Law, J. (2004) Embodied action, enacted bodies: the example of hypoglycaemia. Body & Society, 10, 43-62.

Personal digital data as a companion species

Update: I have now published a journal article that brings this post together with the following post on ‘eating’ digital data – the article can be found here.

While an intense interest in digital data in popular and research cultures is now evident, we still know little about how humans interacting with, making sense of and using the digital data that they generate. Everyday data practices remain under-researched and under-theorised. In attempting to identify and think through some of the ways in which critical digital data scholars may seek to contribute to understandings of data practices, I am developing an argument that rests largely on the work of two scholars in the field of science and technology studies: Donna Haraway and Annemarie Mol. In this post I begin with Haraway, while my next post will discuss Mol.

Haraway’s work has often attempted ‘to find descriptive language that names emergent ontologies’, and I use her ideas here in the spirit of developing new terms and concepts to describe humans’ encounters with digital data. Haraway emphasises that humans cannot be separated from nonhumans conceptually, as we are constantly interacting with other animals and material objects as we go about our daily lives. Her writings on the cyborg have been influential in theory for conceptualising human and computer technological encounters (Haraway, 1991). In this work, Haraway drew attention to the idea that human ontology must be understood as multiple and dynamic rather than fixed and essential, as blurring boundaries between nature and culture, human and nonhuman, Self and Other. She contends that actors, whether human or nonhuman, are never pre-established; rather they emerge through relational encounters (Bhavnani and Haraway, 1994). The cyborg metaphor encapsulates this idea, not solely in relation to human-technology assemblages but to any interaction of humans with nonhumans.

This perspective already provides a basis for thinking through the emergent ontologies that are the digital data assemblages that are configured by humans’ interactions with the software and hardware that generate digital data about them. Haraway’s musings on human and nonhuman animal interactions (Haraway, 2003, 2008, 2015) also have resonance for how we might understand digital data-human assemblages. Haraway uses the term ‘companion species’ to describe the relationships that the human species has not only with other animal species but also with technologies. Humans are companion species with the nonhumans with which they live alongside and engage, each species learning from and influencing the other, co-evolving. Haraway refers to companion species as ‘post-cyborg entities’, acknowledging the development of her thinking since her original cyborg exegesis.

This trope of companion species may be taken up to think about the ways in which humans generate, materialise and engage with digital data. Thrift has described the new ‘hybrid beings’ that are comprised of digital data and human flesh. Adopting Haraway’s companion species trope allows for the extension of this idea by acknowledging the liveliness of digital data and the relational nature of our interactions with these data. Haraway has commented in a lecture that she has learnt

through my own inhabiting of the figure of the cyborg about the non-anthropomorphic agency and the liveliness of artifacts. The kind of sociality that joins humans and machines is a sociality that constitutes both, so if there is some kind of liveliness going on here it is both human and non-human. Who humans are ontologically is constituted out of that relationality.

This observation goes to the heart of how we might begin to theorise the liveliness of digital data in the context of our own aliveness/liveliness, highlighting the relationality and sociality that connect them.

Like companion species and their humans, digital data are lively combinations of nature/culture. Digital data are lively in several ways. They are about life itself (details about human’s and other living species), they are constantly generated and regenerated as well as purposed and repurposed as they enter into the digital knowledge economy, they have potential impacts on humans’ and other species’ lives via the assumptions and inferences that they are used to develop and they have consequences for livelihoods in terms of their commercial and other value and effects.

Rather than think of the contemporary digitised human body/self as posthuman (cf. Haraway’s comments on posthumanism in her interview with Gane, 2006), the companion species perspective develops the idea of ‘co-human’ entities. Just as digital data assemblages are comprised of specific information points about people’s lives, and thus learn from people as algorithmic processes manipulate this personal information, people in turn learn from the digital data assemblages of which they are a part. The book choices that Amazon offers the, the ads that are delivered to them on Facebook or Twitter, the returns that are listed from search engine queries or browsing histories, the information that a fitness trackers provides about their heart rate or calories burnt each day are all customised to their digitised behaviours. Perusing these data can provide people with insights about themselves and may structure their future behaviour.

These aspects of digital data assemblages are perhaps becoming even more pronounced as the Internet of Things develops and humans become just one node in a network of smart objects that configure and exchange digital data with each other. Humans move around in data-saturated environments and they are able to wear personalised data-generating devices on their bodies, including not only their smartphones but objects such as sensor-embedded wristbands, clothing or watches. The devices that we carry with us literally are our companions: in the case of smartphones regularly touched, fiddled with and looked at throughout the day. But in distinction from previous technological prostheses, these mobile and wearable devices are also invested with and send out continuous flows of personal information. They have become the repositories of communication with others, geolocation information, personal images, biometric information and more. They also leak these data outwards as they are transmitted to computing cloud servers. All this is happening in real-time and continuously, raising important questions about the security and privacy of the very intimate information that these devices generate, transmit and archive (Tene and Polonetsky, 2013).

The companion species trope recognises the inevitability of our relationship with our digital data assemblages and the importance of learning to live together and to learn from each other. It suggests both the vitality of these assemblages and also the possibility of developing a productive relationship, recognising our mutual dependency. We may begin to think about our digital data assemblages as members of a companion species that have lives of their own that are beyond our complete control. These proliferating digital data companion species, as they are ceaselessly configured and reconfigured, emerge beyond our bodies/selves and into the wild of digital data economies and circulations. They are purposed and repurposed by second and third parties and even more actors beyond our reckoning as they are assembled and reassembled. Yet even as our digital data companion species engage in their own lives, they are still part of us and we remain part of them. We may interact with them or not; we may be allowed access to them or not; we may be totally unaware of them or we may engage in purposeful collection and use of them. They have implications for our lives in a rapidly growing array of contexts, from the international travel we are allowed to undertake to the insurance premiums, job offers or credit we are offered.

If we adopt Haraway’s companion species trope, we might ask the following: What are our affective responses to our digital data companion species? Do we love or hate them, or simply feel indifferent to them? What are the contexts for these responses? How do we live with our digital data companion species? How do they live with us? How do our lives intersect with them? What do they learn from us, and what do we learn from them? What is the nature of their own lives as they move around the digital data economy? How are we influenced by them? How much can we domesticate or discipline them? How do they domesticate or discipline us? How does each species co-evolve?

References

Bhavnani, K.-K. & Haraway, D. (1994) Shifting the subject: a conversation between Kum-Kum Bhavnani and Donna Haraway, 12 April 1993, Santa Cruz, California. Feminism & Psychology, 4, 19-39.

Gane, N. (2006) When we have never been human, what is to be done?: Interview with Donna Haraway. Theory, Culture & Society, 23, 135-58.

Haraway, D. (1991) Simians, Cyborgs and Women: the Reinvention of NatureLondon: Free Association.

Haraway, D. (2003) The Companion Species Manifesto: Dogs, People, and Significant Otherness. Chicago: Prickly Paradigm.

Haraway, D. (2008) When Species Meet. Minneapolis: The University of Minnesota Press.

Tene, O. & Polonetsky, J. (2013) Big data for all: Privacy and user control in the age of analytics. Northwestern Journal of Technology & Intellectual Property, 11, 239-73.

The thirteen Ps of big data

Big data are often described as being characterised by the ‘3 Vs’: volume (the large scale of the data); variety (the different forms of data sets that can now be gathered by digital devices and software); and velocity (the constant generation of these data). An online search of the ‘Vs’ of big data soon reveals that some commentators have augmented these Vs with the following: value (the opportunities offered by big data to generate insights); veracity/validity (the accuracy/truthfulness of big data); virality (the speed at which big data can circulate online); and viscosity (the resistances and frictions in the flow of big data) (see Uprichard, 2013 for a list of even more ‘Vs’).

These characterisations principally come from the worlds of data science and data analytics. From the perspective of critical data researchers, there are different ways in which big data can be described and conceptualised (see the further reading list below for some key works in this literature). Anthropologists Tom Boellstorff and Bill Maurer (2015a) refer to the ‘3 Rs’: relation, recognition and rot. As they explain, big data are always formed and given meaning via relationships with human and nonhuman actors that extend beyond data themselves; how data are recognised qua data is a sociocultural and political process; and data are susceptible to ‘rot’, or deterioration or unintended transformation as they are purposed and repurposed, sometimes in unintended ways.

Based on my research and reading of the critical data studies literature, I have generated my own list that can be organised around what I am choosing to call the ‘Thirteen Ps’ of big data. As in any such schema, this ‘Thirteen Ps’ list is reductive, acting as a discursive framework to organise and present ideas. But it is one way to draw attention to the sociocultural dimensions of big data that the ‘Vs’ lists have thus far failed to acknowledge, and to challenge the taken-for-granted attributes of the big data phenomenon.

1. Portentous: The popular discourse on big data tends to represent the phenomenon as having momentous significance for commercial, managerial, governmental and research purposes.
2. Perverse: Representations of big data are also ambivalent, demonstrating not only breathless excitement about the opportunities they offer but also fear and anxiety about not being able to exert control over their sheer volume and unceasing generation and the ways in which they are deployed (as evidenced in metaphors of big data that refer to ‘deluges’ and ‘tsunamis’ that threaten to overwhelm us).
3. Personal: Big data incorporate, aggregate and reveal detailed information about people’s personal behaviours, preferences, relationships, bodily functions and emotions.
4. Productive: The big data phenomenon is generative in many ways, configuring new or different ways of conceptualising, representing and managing selfhood, the body, social groups, environments, government, the economy and so on.
5. Partial: Big data can only ever tell a certain narrative, and as such they offer a limited perspective. There are many other ways of telling stories using different forms of knowledges. Big data are also partial in the same way as they are relational: only some phenomena are singled out and labelled as ‘data’, while others are ignored. Furthermore, more big data are collected on some groups than others: those people who do not use or have access to the internet, for example, will be underrepresented in big digital data sets.
6. Practices: The generation and use of big data sets involve a range of data practices on the part of individuals and organisations, including collecting information about oneself using self-tracking devices, contributing content on social media sites, the harvesting of online transactions by the internet empires and the data mining industry and the development of tools and software to produce, analyse, represent and store big data sets.
7. Predictive: Predictive analytics using big data are used to make inferences about people’s behaviour. These inferences are becoming influential in optimising or limiting people’s opportunities and life chances, including their access to healthcare, insurance, employment and credit.
8. Political: Big data is a phenomenon that involves power relations, including struggles over ownership of or access to data sets, the meanings and interpretations that should be attributed to big data, the ways in which digital surveillance is conducted and the exacerbation of socioeconomic disadvantage.
9. Provocative: The big data phenomenon is controversial. It has provoked much recent debate in response to various scandals and controversies related to the digital surveillance of citizens by national security agencies, the use and misuse of personal data, the commercialisation of data and whether or not big data poses a challenge to the expertise of the academic social sciences.
10. Privacy: There are growing concerns in relation to the privacy and security of big data sets as people are becoming aware of how their personal data are used for surveillance and marketing purposes, often without their consent or knowledge and the vulnerability of digital data to hackers.
11. Polyvalent: The social, cultural, geographical and temporal contexts in which big data are generated, purposed and repurposed by a multitude of actors and agencies, and the proliferating data profiles on individuals and social groups that big data sets generate give these data many meanings for the different entities involved.
12. Polymorphous: Big data can take many forms as data sets are generated, combined, manipulated and materialised in different ways, from 2D graphics to 3D-printed objects.
13. Playful: Generating and materialising big data sets can have a ludic quality: for self-trackers who enjoy collecting and sharing information on themselves or competing with other self-trackers, for example, or for data visualisation experts or data artists who enjoy manipulating big data to produce beautiful graphics.

Critical Data Studies – Further Reading List

Andrejevic, M. (2014) The big data divide, International Journal of Communication, 8,  1673-89.

Boellstorff, T. (2013) Making big data, in theory, First Monday, 18 (10). <http://firstmonday.org/ojs/index.php/fm/article/view/4869/3750&gt;, accessed 8 October 2013.

Boellstorff, T. & Maurer, B. (2015a) Introduction, in T. Boellstorff & B. Maurer (eds.), Data, Now Bigger and Better! (Chicago, IL: Prickly Paradigm Press), 1-6.

Boellstorff, T. & Maurer, B. (eds.) (2015b) Data, Now Bigger and Better! Chicago, IL: Prickly Paradigm Press.

boyd, d. & Crawford, K. (2012) Critical questions for Big Data: provocations for a cultural, technological, and scholarly phenomenon, Information, Communication & Society, 15 (5),  662-79.

Burrows, R. & Savage, M. (2014) After the crisis? Big Data and the methodological challenges of empirical sociology, Big Data & Society, 1 (1).

Cheney-Lippold, J. (2011) A new algorithmic identity: soft biopolitics and the modulation of control, Theory, Culture & Society, 28 (6),  164-81.

Crawford, K. & Schultz, J. (2014) Big data and due process: toward a framework to redress predictive privacy harms, Boston College Law Review, 55 (1),  93-128.

Gitelman, L. & Jackson, V. (2013) Introduction, in L. Gitelman (ed.), Raw Data is an Oxymoron. Cambridge, MA: MIT Press, pp. 1-14.

Helles, R. & Jensen, K.B. (2013) Making data – big data and beyond: Introduction to the special issue, First Monday, 18 (10). <http://firstmonday.org/ojs/index.php/fm/article/view/4860/3748&gt;, accessed 8 October 2013.

Kitchin, R. (2014) The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. London: Sage.

Kitchin, R. & Lauriault, T. (2014) Towards critical data studies: charting and unpacking data assemblages and their work, Social Science Research Network. <http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2474112&gt;, accessed 27 August 2014.

Lupton, D. (2015) ‘Chapter 5: A Critical Sociology of Big Data’ in Digital Sociology. London: Routledge.

Lyon, D. (2014) Surveillance, Snowden, and Big Data: Capacities, consequences, critique, Big Data & Society, 1 (2). <http://bds.sagepub.com/content/1/2/2053951714541861&gt;, accessed 13 December 2014.

Madden, M. (2014) Public Perceptions of Privacy and Security in the post-Snowden Era, Pew Research Internet Project: Pew Research Center.

McCosker, A. & Wilken, R. (2014) Rethinking ‘big data’ as visual knowledge: the sublime and the diagrammatic in data visualisation, Visual Studies, 29 (2),  155-64.

Robinson, D., Yu, H., and Rieke, A. (2014) Civil Rights, Big Data, and Our Algorithmic Future. No place of publication provided: Robinson + Yu.

Ruppert, E. (2013) Rethinking empirical social sciences, Dialogues in Human Geography, 3 (3),  268-73.

Tene, O. & Polonetsky, J. (2013) A theory of creepy: technology, privacy and shifting social norms, Yale Journal of Law & Technology, 16,  59-134.

Thrift, N. (2014) The ‘sentient’ city and what it may portend, Big Data & Society, 1 (1). <http://bds.sagepub.com/content/1/1/2053951714532241.full.pdf+html&gt;, accessed 1 April 2014.

Tinati, R., Halford, S., Carr, L., and Pope, C. (2014) Big data: methodological challenges and approaches for sociological analysis, Sociology, 48 (4),  663-81.

Uprichard, E. (2013) Big data, little questions?, Discover Society,  (1). <http://www.discoversociety.org/focus-big-data-little-questions/&gt;, accessed 28 October 2013.

van Dijck, J. (2014) Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology, Surveillance & Society, 12 (2),  197-208.

Vis, F. (2013) A critical reflection on Big Data: considering APIs, researchers and tools as data makers, First Monday, 18 (10). <http://firstmonday.org/ojs/index.php/fm/article/view/4878/3755&gt;, accessed 27 October 2013.

The politics of privacy in the digital age

The latest except from my forthcoming book Digital Sociology (due to be released by Routledge on 12 November 2014). This one is from Chapter 7: Digital Politics and Citizen Digital Public Engagement.

The distinction between public and private has become challenged and transformed via digital media practices. Indeed it has been contended that via the use of online confessional practices, as well as the accumulating masses of data that are generated about digital technology users’ everyday habits, activities and preferences, the concept of privacy has changed. Increasingly, as data from many other users are aggregated and interpreted using algorithms, one’s own data has an impact on others by predicting their tastes and preferences (boyd, 2012). The concept of ‘networked privacy’ developed by danah boyd (2012) acknowledges this complexity. As she points out, it is difficult to make a case for privacy as an individual issue in the age of social media networks and sousveillance. Many people who upload images or comments to social media sites include other people in the material, either deliberately or inadvertently. As boyd (2012: 348) observes, ‘I can’t even count the number of photos that were taken by strangers with me in the background at the Taj Mahal’.

Many users have come to realise that the information about themselves and their friends and family members that they choose to share on social media platforms may be accessible to others, depending on the privacy policy of the platform and the ways in which users have operated privacy settings. Information that is shared on Facebook, for example, is far easier to limit to Facebook friends if privacy settings restrict access than are data that users upload to platforms such as Twitter, YouTube or Instagram, which have few, if any, settings that can be used to limit access to personal content. Even within Facebook, however, users must accept that their data may be accessed by those that they have chosen as friends. They may be included in photos that are uploaded by their friends even if they do not wish others to view the photo, for example.

Open source data harvesting tools are now available that allow people to search their friends’ data. Using a tool such as Facebook Graph Search, people who have joined that social media platform can mine the data uploaded by their friends and search for patterns. Such elements as ‘photos of my friends in New York’ or ‘restaurants my friends like’ can be identified using this tool. In certain professions, such as academia, others can use search engines to find out many details about one’s employment details and accomplishments (just one example is Google Scholar, which lists academics’ publications as well as how often and where they have been cited by others). Such personal data as online photographs or videos of people, their social media profiles and online comments can easily be accessed by others by using search engines.

Furthermore, not only are individuals’ personal data shared in social networks, they may now be used to make predictions about others’ actions, interests, preferences or even health states (Andrejevic, 2013; boyd, 2012). When people’s small data are aggregated with others to produce big data, the resultant datasets are used for predictive analytics (Chapter 5). As part of algorithmic veillance and the production of algorithmic identities, people become represented as configurations of others in the social media networks with which they engage and the websites people characterised as ‘like them’ visit. There is little, if any, opportunity to opt out of participation in these data assemblages that are configured about oneself.

A significant tension exists in discourses about online privacy. Research suggests that people hold ambivalent and sometimes paradoxical ideas about privacy in digital society. Many people value the use of dataveillance for security purposes and for improving economic and social wellbeing. It is common for digital media users to state that they are not concerned about being monitored by others online because they have nothing to hide (Best, 2010). On the other hand, however, there is evidence of unease about the continuous, ubiquitous and pervasive nature of digital surveillance. It has become recognised that there are limits to the extent to which privacy can be protected, at least in terms of individuals being able to exert control over access to digital data about themselves or enjoy the right to be forgotten (Rosen, 2012; Rosenzweig, 2012). Some commentators have contended that notions of privacy, indeed, need to be rethought in the digital era. Rosenzweig (2012) has described previous concepts as ‘antique privacy’, which require challenging and reassessment in the contemporary world of ubiquitous dataveillance. He asserts that in weighing up rights and freedoms, the means, ends and consequences of any dataveillance program should be individually assessed.

Recent surveys of Americans by the Pew Research Center (Rainie and Madden, 2013) have found that the majority still value the notion of personal privacy but also value the protection against criminals or terrorists that breaches of their own privacy may offer. Digital technology users for the most part are aware of the trade-off between protecting their personal data from others’ scrutiny or commercial use, and gaining benefits from using digital media platforms that collect these data as a condition of use. This research demonstrates that the context in which personal data are collected is important to people’s assessments of whether their privacy should be intruded upon. The Americans surveyed were more concerned about others knowing the content of their emails than their internet searches, and were more likely to experience or witness breaches of privacy in their own social media networks than to be aware of government surveillance of their personal data.

Another study using qualitative interviews with Britons (The Wellcome Trust, 2013) investigated public attitudes to personal data and the linking of these data. The research found that many interviewees demonstrated a positive perspective on the use of big data for national security and the prevention and detection of crime, improving government services, the allocation of resources and planning, identifying social and population trends, convenience and time-saving when doing shopping and other online transactions, identifying dishonest practices and making vital medical information available in an emergency. However the interviewees also expressed a number of concerns about the use of their data, including the potential for the data to be lost, stolen, hacked or leaked and shared without consent, the invasion of privacy when used for surveillance, unsolicited marketing and advertising, the difficulty of correcting inaccurate data on oneself and the use of the data to discriminate against people. Those interviewees of low socioeconomic status were more likely to feel powerless about dealing with potential personal data breaches, identity theft or the use of their data to discriminate against them.

References

Andrejevic, M. (2013) Infoglut: How Too Much Information is Changing the Way We Think and KnowNew York: Routledge.

Best, K. (2010) Living in the control society: surveillance, users and digital screen technologies. International Journal of Cultural Studies, 13, 5-24.

boyd, d. (2012) Networked privacy. Surveillance & Society, 10, 348-50.

Rainie, L. & Madden, M. (2013) 5 findings about privacy. http://networked.pewinternet.org/2013/12/23/5-findings-about-privacy, accessed 24 December 2013.

Rosen, J. (2012) The right to be forgotten. Stanford Law Review Online, 64 (88). http://www.stanfordlawreview.org/online/privacy-paradox/right-to-be-forgotten/, accessed 21 November 2013.

Rosenzweig, P. (2012) Whither privacy? Surveillance & Society, 10, 344-47.

The Wellcome Trust (2013) Summary Report of Qualitative Research into Public Attitudes to Personal Data and Linking Personal Data [online text], The Wellcome Trust http://www.wellcome.ac.uk/stellent/groups/corporatesite/@msh_grants/documents/web_document/wtp053205.pdf

 

New project on fitness self-tracking apps and websites

My colleague Glen Fuller and I have started a new project on people’s use of fitness self-tracking apps and platforms (such as Strava and RunKeeper). We are interviewing people who are active users of these devices, seeking to identify why they have chosen to take up these practices, what apps and platforms they use, how they use them and what they do with the personal data that are generated from these technologies. We are interested in exploring issues around identity and self-representation, concepts of health, fitness and the body, privacy, surveillance and data practices and cultures.

The city in which we live and work, Canberra, is an ideal place to conduct this project, as there are many ardent cyclists and runners living here.

See here for our project’s website and further details of the study.

Call for papers: Big Data Cultures symposium

I am convening a one-day symposium to be held on Monday 15 September 2014 that addresses the social, cultural, political and ethical issues and implications of the big data phenomenon. It will be held by the News & Media Research Centre, University of Canberra, Australia.

A keynote speaker will open proceedings (details to be confirmed), but paper abstracts from any interested contributors are invited for consideration. Appropriate topics may include, but are not limited to, the following areas:

– privacy, security and legal issues
– how big data are changing forms of governance and commercial operations
– big data ecosystems
– the open data/citizen data movement
– data hactivism and queering big data
– public understandings of big data
– surveillance and big data
– creative forms of data visualisation
– self-tracking and the quantified self
– data doubles and data selves
– the materiality of digital data
– the social lives of digital data-objects
– algorithmic identities and publics
– code acts
– responses to big data from artists and designers

Abstracts of 150-200 words should be submitted to me (deborah.lupton@canberra.edu.au) by 1 July 2014 for consideration for inclusion in the symposium. Please contact me if you require any further information.