Datafied times: Surveillance capitalism,data technologies and the social construction of time in family life

Data technologies and the time regime of surveillance capitalism

In November, 2017 Chamath Palihapitiya, a former Vice President at Facebook spoke about his ‘tremendous guilt’ about growing the social network. In his talk at Stanford University, he told the audience that social media were impacting negatively on society because, like drugs, they were designed to make people addicted by enabling dopamine-driven feedback loops (Wang, 2017). A year later, the BBC’s Panorama, a British investigative current affairs programme, investigated the issue further and came to the conclusion that tech-giants are deliberately designing technologies that are addictive. One example that they mention in the programme is the technology of ‘infinite scroll’, which allows users to endlessly swipe down through content without clicking, and by doing so it favours addictive practices (Andersson, 2018). The BBC interviewed the engineer of infinite scrolling, Mr Raskin, who had worked for Mozilla and Jawbone and who confirmed that the technology was designed to make sure that users were hooked and spent the most time possible on their phones, producing more and more data.

In an article published in the Business Insider (Hamilton, 2018), Professor Przybylski, an experimental psychologist and Director of Research at the Oxford Internet Institute, together with other psychologists cautioned against simplified understandings of psychology and addiction. In fact, they contend not only that technologies cannot really be compared to drug or alcohol abuse, and that associating them could be risky, but also that computer engineers, like Mr Raskin, relied on psychological research that had been unproven or criticized.

When I read the debate about addictive designs, I was struck by the fact that these discussions had a long history, and that they had less to say about ‘technological addiction’ and more to say about the complex relationship between capitalism, technology and time. Computer engineers like Mr Palihapitiya and Mr Raskin claimed that they were designing technologies to influence how users spent their time in order to create value. The more time users spent on their devices, the more data they would generate, which in turn would create more value for the digital economy. Hence, it was essential, from the perspective of the developers, to design technologies that would guarantee some form of dependence or the illusion of it.

The practice of controlling people’s time through technological use in order to create more value has defined the history of capitalism. Since the very early days of capitalism, technologies have always been used to control people’s temporal practices and create more economic value. The relationship between capitalism, technologies and the social construction of time dates back to the introduction of clock time. In the Middle Ages in England, there were only few areas of exact time keeping – such as monasteries or towns – and time was still organized around specific agricultural or social activities. However, from the 14th century onwards, with the protestant ethics and the rise of earlier forms of capitalism, the ‘time of the merchant’ gradually took hold over other dimensions of time, with clocks entering households, and taking over church bells in organizing the everyday lives of people (Thompson, 1967: 82–86).

The synchronization of human activities and clock time were all necessary for the economy of the modern, industrial nation state. For Marx (1990), clock time was introduced to organize and manage labour time under capitalism, which had become a valuable commodity, and to maximize production, both of which were essential to capitalist accumulation. For Weber (1978), clock time enabled an increased synchronization of human activities, which was key to the rationalization process of the modern nation state. In understanding the transformation brought about by clock time, we need to consider how the change in political economic structures and institutions – highlighted by Marx and Weber – triggered a fundamental transformation in society.

The 1970s and 1980s gave rise to a new type of economy, one that no longer relied on the manufacturing industry, but instead created wealth and value from the service industry. The new economy was fuelled by a de-regulated financial sector and the possibilities offered by the globalization of markets (Giddens, 1991; Harvey, 1990). The new global economy – thanks to the rapid expansion of Internet technologies – introduced a new type of temporal regime that guaranteed that working routines were no longer dictated by clock time like in the factory, but by a self-regulating flexibility, which eroded the boundary between labour time and leisure time (Fuchs, 2014; Gill and Pratt, 2008; Gregg, 2011). In the understanding of Internet time, some scholars have focused on how online technologies have become the material support for the establishment of new forms of immediacy and social acceleration (Fuchs, 2014; Hassan, 2007, 2009; Kaun, 2015; Rosa, 2003, 2013; Rosa and Scheuerman, 2009; Tomlinson, 2007; Barassi, 2015a, 2015b), while others focused on co-presence and instant fulfilment (Leong et al., 2009; Petranker, 2007; Tomlinson, 2007).

The early 2000s, have brought another key transformation in capitalism, with the rise of surveillance capitalism. According to Bellamy et al. (2014), surveillance capitalism established itself over the last century as a political economic system defined by the relations of power between governments, military powers, secret agencies, the financial sector, advertisers, Internet monopolies and multiple other agents who surveilled, controlled and capitalized on individual data. The rise of big data was thus made possible by the ever-growing networked infrastructure of surveillance capitalism that constantly sought new ways to turn personal data into profit (Zuboff, 2015).

At present there is much emerging research on the social and political implications of surveillance capitalism yet research on its time regime is still scarce, and the little understanding of the ways in which this time regime is established through data technologies. In understanding the time regime of surveillance capitalism and how it is being reinforced by data technologies, we need to bear in mind the fact that mediated time is not characterized by a singular speed; it is defined by the juncture of different temporal modes and rhythms (Keightley, 2013: 67). Hence, here I want to consider the interconnection of three different hegemonic temporalities – immediacy, archival time and predictive time – and to explore how these temporalities are reproduced through technological design and experienced in everyday life.

The time regime of surveillance capitalism: immediate, archival and predictive time

When we think about the relationship between surveillance capitalism and data technologies, there are at least three different yet interconnected hegemonic temporalities that we need to take into account. The first is immediacy. The inclusion of data technologies in different areas of social life is based on the assumption that we need to instantaneously surveille life as it unfolds and that we can record and hence control different dimensions of everyday life through data tracking technologies. This later point emerges well in Kitchin (2014a) work on smart cities and real time as well as in Lyon’s (2018) latest work on surveillance culture. Both highlight the current tendency to create structures and organize practices that enable the real-time monitoring of everyday life.

The temporality of immediacy has long been studied with reference to Internet time. According to Hassan (2003, 2007, 2009), Internet technologies have created the shared impression that we live in a continuous present, a hyper now, where past and future are subservient to the logic of the present (p. 103). As Tomlinson (2007) has argued, immediacy needs to be understood as carrying two interconnected cultural understandings. On the one hand, it serves to indicate the compression of space and a sense of ‘proximity’ (from the Latin, immediate). On the other hand, it serves to specify the compression of time and the notion of ‘instantaneity’. Drawing on the work of Bauman (2005), Tomlinson (2007), therefore, concludes that ‘immediacy’ is built on a notion of instantaneous contact and immediate fulfilment (p. 91).

With the rise of surveillance capitalism, immediacy came to have a new meaning. This is because online technologies did not only enable instantaneous communication and immediate fulfilment, but they also enabled the continuous production, storage and processing of the new oil: personal data. Under surveillance capitalism, data technologies have introduced a new time regime that valorises continuous productivity (Fuchs, 2014) and buries any unproductive communicator (Elmer, 2004). This is what Mr Raskin was talking about. The developers of data technologies need to make sure that users keep on producing more and more data, because data generates value.

Under surveillance capitalism, data technologies are not simply designed to enable the constant production of data, they are ‘also comprised of vast server farms, rooms of computers humming away aimed at the rationalized storage of vast amounts of data’(Gehl, 2011: 1229). Therefore, another fundamental temporal dimension of data technologies is represented by archival time. Today, every little detail of everyday life is captured, archived and turned into a data point. One of the big changes brought by surveillance capitalism is the introduction of the cultural belief that data offer us a deeper form of knowledge. As Mayer-Schönberger and Cukier (2013) explained, in the last decade, data were no longer regarded as static or stale, it became a raw material of business, a vital economic input, used to create a new form of economic value (p. 5). It is for this reason that we have seen a radical transformation in society. This is because governing institutions, educational bodies, healthcare providers, businesses of all kinds and multiple other agents have started to turn every aspect of everyday life into data and to archive it (Mayer-Schönbergerand Cukier, 2013).

We cannot really understand this transformation without looking at another fundamental temporality that defines the very design of data technologies: predictive time. The promise (and value) of technological ideas such as machine learning, neural networks or artificial intelligence lies precisely in predictive analytics: the understanding that the aggregation of data can highlight behavioural patterns, which then can enable companies to somehow ‘predict the future’ and to mitigate risk (Lohr, 2015). Although it has become clear that algorithmic predictions are often biased, discriminatory and wrong (Eubanks, 2018; Noble, 2018; O’Neil, 2016), under surveillance capitalism, predictive analytics is used everywhere, by educators in schools who believe in creating personalized education, by banks, insurers and recruiters who need to decide loans, premiums or whether one is a good fit for a job or not. Predictive analytics is also used by the police (Dencik et al., 2018), immigration enforcement and governmental institutions who decide a variety of issues from child protection to social welfare (Eubanks, 2018), and of course by secret services.

As Aradau and Blanke (2017) have shown, predictive analytics is today imagined as a reservoir of unexpected insights which can transform practices of governance (p. 379). Aradau and Blanke’s (2017) work is important to the understanding of predictive time, because they draw on Foucault to demonstrate that data technologies enable the surveillance and disciplining of bodies through space and time. What is fascinating about predictive ‘predictive time’ is the fact that it brings past, present and future together into a unique temporal dimension. Predictive time as both a hegemonic temporality and temporalizing practice distinguishes itself because it draws on past data traces to inform present decisions in order to mitigate future risks. Different scholars have shown the democratic challenges of this form of temporality especially in relation to predictive policing. Aradau and Blanke (2017) for instance, explore the relationship between predictive policing and disciplining. Dencik et al. (2016) show how predictive policing and data governance are defined by three fundamental challenges: the inclusion of pre-existing biases and agendas in algorithms, the prominence of marketing-driven software and inability of interpreting and dealing with unpredictability.

It is through their interconnection of immediate, archival and predictive time that data technologies act as disciplining technologies for surveillance capitalism. Time is an important dimension of any disciplining technology of surveillance. As Aradau and Blanke (2017) have argued, the very understanding of the panopticon, as developed by Foucault (2015, cited in Aradau and Blanke [2017]) as a space of total surveillance is constituted through time; surveillance adds up and capitalizes on time (p. 377). It is by surveilling an individual in space and time that surveillance becomes more effective and individuals can be governed (Lyon, 2001).

If we really want to understand the time regime of surveillance capitalism, we need to explore the ways in which its different hegemonic temporalities are reproduced in everyday practice through the use of data technologies. Hence, we need to look at the notion of temporalizing practice.

Hegemonic temporalities and everyday temporalizing practices

One particularly interesting element that emerges in the literature on technology and the social construction of time is the focus on social practice. This emerges well in the works of Thompson (1967), Thrift (1990) and Elias (1993). Influenced by both Marx and Weber, Thompson (1967) explored how between the 14th century and the 19th century the gradual synchronization of human activities in Europe was largely made possible by the combination of technical developments on the one side and an institutionally driven organization of people’s behaviours on the other. Also, according to Thrift (1990), the gradual diffusion of a new type of ‘time-consciousness’, where ‘clock time’ was established as the hegemonic form of time measurement, was made possible through a process of propaganda aimed at ‘civilizing’ the working classes and transforming their daily routines. This process was enabled through institutions such as ‘the factory’ or ‘the school’, which used incentives, fines and other strategies to transform people’s behaviours (Thompson, 1967: 90–95).

It is for this reason that Elias (1993), who was influenced by Bourdieu (1964), argued that social time – especially in Europe – operated as a form of social habitus that was linked to broader processes of ‘civilization’ and manners, which developed with the rise of the ‘merchant’ society and the earlier forms of capitalism. According to Elias (1993), social time is tightly connected to forms of self-regulation, and is perceived subjectively as part of everyday human experience. The understanding that there is a bound connection between hegemonic constructions of social time and people’s everyday subjective experience and practice is essential in the analysis of the temporality of data technologies. This is because it enables us to look at how people and cultures internalize and (and resist) hegemonic temporalities through technological use. Consequently, we need to understand technological use as ‘temporalising practice’ (Barassi, 2015a).

I have personally explored these everyday temporalizing practices by looking at the use of data technologies in family life. In 2016, I launched a project, which aimed at critically investigating the impact of big data and AI on family life. The research relied on a multi-method approach that combined 2 years of auto-ethnographic research; 50 semi-structured in-depth interviews with parents with children from 0 to 13 years of age (whose personal information is regulated by Child Online Privacy Protection Act); 8 months of digital ethnography of parents ‘sharenting’ practices on the social media accounts of eight families; and the platform analysis of four social media platforms; 10 apps (baby apps and pregnancy apps); 4 home hubs; 4 education platforms.

Methodologically, the project was based on the belief that in order to understand the multiple and complex ways in which big data and AI is transforming family life, we need to consider the relationship between UK and US contexts. As the mother of young girls, I carried out 2 years of auto-ethnography and participant observation, documenting what it felt to live in two different cities like London and Los Angeles with different data environments. I investigated how my children were being datafied and how I and the people around me reacted to these processes of datafication.

I also interviewed parents who came from a variety of cultural, ethnic and national backgrounds. The parents were extraordinarily diverse not only in terms of ethnicity (I interviewed White, Asian, Latinos, Indian, Black, Indigenous, Multiracial) but also in terms of cultural and national heritage (Afghani, Mexican, Brazilian, Indian, German, Italian, Hungarian, Icelandic, Zimbabwe, Scottish, etc.). I also made a genuine attempt to interview parents from different classes – by interviewing parents working on low-income jobs and high-income jobs – and I sought to interview parents who challenged the ‘normative nuclear family’ by interviewing same-sex parents, single parents or divorced parents, who managed complex living arrangements.

It was during this multi-method research project that I investigated how families – through their everyday technological use – were reproducing and negotiating with the hegemonic temporalities of surveillance capitalism. Here below I will explore their lived experience and understandings by focusing on three different but related themes: Immediacy and techno-dependency; data tracking and archival time; predictive time and data futures.

Immediacy, techno-dependency and living in datafied times

During the research, I asked parents to describe their digital routines. I deliberately decided to ask parents to talk about their digital routines not on days when they had to go to work, but on weekends or holidays, when they could disconnect and focus on family life. The testimonies that I collected were all very similar and talked directly about the fact that daily family life today is entirely shaped by digital routines. From the moment in which parents wake up and reach for the phone, to the moment in which they find themselves, in the evenings, sitting on the sofa ‘double screening’.

When describing their daily digital routines, parents could remember a time in which ‘You didn’t have to wake up in the morning and go online’ or you ‘chose to go online’. Sonia, the mother of a 6-month-old baby who lived in London, told me that in the past she could ‘decide’ to go online, while today it is totally different:

I bought my iPhone in 2009, at the time I wasn’t as connected as I am now, it is integrated into life now. Before you had to make a decision if you wanted to go on the internet, now you are constantly connected. . .Now I check my phone constantly, if I wasn’t sitting here with you I would be on my phone. I try to make a conscious effort not to do it, but I can’t tell you how much I look at my phone.

In describing this change, parents often talked about technological use as a ‘need’, as a ‘compulsion’ and as an ‘addiction’; they blamed themselves and their partners for what they perceived as excessive technological use. One day, for instance, I was talking to Mike, the father of a 5-year-old, and he discussed how his wife’s technological use was impacting on their everyday life:

The ‘narrative of techno-dependency’ is a way in which parents are making sense of their own temporalizing practices, and the way they are constantly reproducing immediacy in everyday life. A great majority of the parents I worked with believed that their and their partner’s relationship with their mobile phones was not healthy and that technologies were ‘stealing time’ away from everyday social interactions. For this reason, they started to take measures to counteract this temporality. Each family had its own tactics to control phone habits, and these tactics varied enormously: some would delete social media apps, some would set specific rules, others would hide their phones. Among all the different practices adopted by parents to control their phone use, what I found particularly fascinating and paradoxical was the fact that at times parents bought apps, such as the Forrest App, that were specifically designed to ‘curb phone use’, apps that would track them and sell their data.

Part of the project was also auto-ethnographic, and so to try and make sense of the narrative of techno-addiction in family life, I started documenting, as Turkle (2011) had done, my own technological compulsion. I clearly remember one particular episode. In June 2017, I was in the car with my family in Los Angeles. The traffic was unbearable, as always. I was 8 months pregnant, and tired. My eldest daughter was looking outside her window playing ‘I spy with my little eye . . .’. I was sitting next to Paul, my husband, playing with my daughter and chatting with him. I told him how proud I was that – unlike other children who are constantly digitally entertained – our daughter would play on her own and was interested in the world. We continued our game. Yet I struggled to keep my hands off my phone. I must have looked at it at least 10 times in a 20-minute drive. What did I check? I do not know. Multiple things I suppose. I checked my emails to see if there was news from a new pre-school that I had just contacted. I went on Twitter to see if anyone reacted to my tweet. I read the UK news and worried about Brexit. I wrote a couple of texts to make plans for the weekend, and checked what bassinet I should buy for my baby. I did all that, while I played ‘I spy’ with my daughter.

Like it happened for Mike and Zoe, also my husband often criticizes me for my techno-addiction. Yet during the research, I came to the conclusion that the narrative of techno-addiction is very difficult to prove, and it distracts us from the fact that we can understand excessive technological use in family life as the expression of what it means to live under surveillance capitalism. Today, immediacy and constant connectedness has come to define our everyday life in ways that were not possible before. This is not only because the technologies that families use in day-to-day life, such as ‘infinite scrolling’, have been designed to ensure that people are constantly connected and that users produce more and more data and generate more income and value. It is also because, one of the main changes brought about by surveillance capitalism is the fact that, the institutions and businesses that individuals encounter in their everyday life (e.g. health providers, education institutions, banks, travelling, leisure activities, local governments etc.) are increasingly relying on digitized and data-driven services.

In our data-driven economies, families have no choice but to constantly be connected and reproduce the temporality of immediacy. Immediacy has become a way of life. It is through constant connectedness that parents can go about their daily life, and sort out daily activities and problems. It is for this reason that the philosopher Floridi (2014) believes that we need to abandon the idea that there is a separation between online and offline. In our data-driven economies, everything has become onlife.

Data tracking in family life and co-surveillance

One day I was interviewing Nicole who lived in West Los Angeles; the house was small and full of the details of a hectic family life, with two girls aged 8 and 10, and three cats. When I arrived, Nicole’s husband Scott was not there; he was out to pick-up the girls from school. We walked through the kitchen into the garden. The sun was coming out after the rain, and we sat down at a dining table overlooking a big tree. Her telephone beeped, she looked up at the tree and told me: ‘the camera told me we are here’. She then explained that they have security cameras in the front and in the back of their house that warn them though an app, when there is movement. After an hour-long interview, where we discussed different issues about digital surveillance and data privacy, we started to chat. She picked up the phone and told me that she wanted to check where her husband was. She opened the app ‘Find my Friends’, a GPS operated app, and told me that she used the app not only to track her husband but also to track her mom, and her dad. ‘Look my dad is in Culver City and my mom at home. I told Scott not to stop for ice-cream when he picked up the girls, but he always does,’ she keeps staring at her phone ‘no, he is still at the school, it looks like he is coming home’.

Nicole told me that she started using the Find My Friends app, when Scott was working in San Diego and was travelling back on Friday nights. She could see where he was or whether something had happened on the way. Few minutes later, the front door bell rang. Nicole went to the door and signed a document. She came back to the garden and immediately received a call from Scott who asked her what she had just signed, because he could see it in the security app. They had a quick argument over the phone, and then she came back to the table.

Nicole and Scott were not the only parents whose everyday life was shaped by similar practices of instantaneous monitoring and co-surveillance. Co-surveillance in family life is largely facilitated by the very structural design of data technologies, and the fact that they can enable family members to surveille one another in real time. This is of course raising critical questions about power and control especially with reference to child–parent relationship. One day, for instance, Lara the mother of five children who worked as a nanny and lived in Los Angeles told me that she would constantly monitor and surveille or children through nanny cams and GPS apps. She explained,

I use nanny cams to be able to watch my children, when I am not home. Even if they are old enough to be alone. But I can watch and interact with them too. I use a GPS on them to know where they are at all times . . . It’s easier because if the kids are out in the neighbourhood I don’t have to go and look for them.

All these examples are key to the understanding of how data technologies are altering the temporal dimension of family life through practices of instantaneous co-surveillance. To understand these practices, we have much to gain if we consider Madianou’s (2016) work on transnational families, where she shows that families rely on a variety of digital technologies (polymedia) to build a sense of co-presence at a distance. Although other scholars have written about co-presence in the past (Leong et al., 2009; Petranker, 2007), Madianou’s work is important because it identifies the fundamental emotional importance of co-presence in everyday family life, yet it also shows that co-presence leads to conflicts because family members feel surveilled and controlled. To understand the impact of data technologies on family life, however, I think that we need to bring together Madianou’s understanding of co-presence with Andrejevic’s (2007) notion of co-surveillance or lateral-surveillance.

According to Andrejevic (2004), thanks to the development in social media and other data technologies, individuals have increasingly adopted practices associated with marketing and law enforcement to gain information about friends, family members and prospective love interests. He also argued that these practices did not displace ‘top-down’ forms of monitoring but in fact emulated and amplified them, fostering the internalization of government strategies and their deployment in the private sphere. Hence, we need to consider the fact that under surveillance capitalism everyday co-surveillance makes people accustomed to be constantly monitored and governed through time (Lyon, 2018).

From co-surveillance to archival time

We cannot understand co-surveillance without taking into account how this practice is linked to broader practices of data tracking in everyday life and hence to the re-production of archival time. Data technologies are reinforcing the cultural value that we need to ‘track’ and ‘document’ everything, and that we constantly need to create data out of everyday experience. The practice of documenting mundane details of everyday life has always existed before the advent of social media and other data technologies (Humphreys, 2018). Yet data technologies have radically expanded and amplified this practice.

The day I interviewed Katie, the mother of a 9-month-old baby and a 13-year-old, who lived in Los Angeles, she told me that data tracking was key to the ‘running of her family life’, and she was particularly grateful for a baby tracking app:

As parents like Katie buy into the promises of data tracking and archival time, they produce more value for surveillance capitalism. This is evident if we consider the fact that, in March 2019, the British Medical Journal published an international research, which demonstrated that out of 24 mHealth apps, 19 shared user data with parent companies and service providers (third parties). The research also showed that the third parties shared user data with 216 ‘fourth parties’ including multinational technology companies, digital advertising companies, telecommunications corporations and a consumer credit reporting agency. Of all these 216 organizations, only three belonged to the health sector. The article also demonstrated that the data were shared with different big-tech companies, including Alphabet (Google), Facebook and Oracle, who occupy central positions within data-broking networks because they have the means to aggregate and re-identify user data (Grundy et al., 2019). Hence, we need to understand that data tracking as a temporalizing practice generates value for surveillance capitalism.

Reproducing and negotiating with predictive time

Predictive analytics is usually a function of artificial intelligence that enables machines to bring different databases together and trace individual patterns (Elmer, 2004), hence it is embedded in the very design of data technologies. Yet, under surveillance capitalism, we need to be aware of the fact that in daily interactions, a plurality of social actors are involved in the temporalizing practice of predictive analytics and try to ‘read’, ‘profile’ and ‘predict’ other people’s behaviours on the basis of data traces. The school headmaster, the employer, the insurer constantly check the data traces of individuals in order to reach present conclusions on their behavioural or psychological characteristics to mitigate future risks. Many of the parents I met during my research often engaged in these practices, either because they worked in education, digital marketing or human resources or because they relied on data traces to decide whether to employ a babysitter or not.

It is because many of the parents that I met were aware of the impact of predictive analytics that they often tactically used technologies and engaged in practices of self-censoring to limit the impact of predictive time on their children. During the 2016 US Presidential Elections, for instance, I met up for a coffee and interview with Jen in West Los Angeles. I had known Jen for few months, and we had different occasions to speak about my research. It was for that reason that she had agreed to help me with my project. As soon as she sat down, and before the interview started, she told me that the night before our interview, her 6-year-old daughter had an emotional meltdown at the prospect that Trump was going to be the next President of the United States.

In the run-up to the elections, Jennifer – who is a democrat – had discussed the situation with her children, and her eldest had become particularly involved and interested in the process. She explained that her daughter was heartbroken when she told her that Trump was probably going to win. She also told me that she felt so proud about her daughters’ political awareness that she decided to film the meltdown. Jennifer wanted to post the video on Facebook, because it was too cute, but her husband Carlos told her that her video would create an important political trace for her daughter, a trace that could define her and impact on her in the future.

During the project, I encountered different parents who, like Jen and Carlos did, were reflecting on the relationship between dataveillance and predictive time, especially by looking into the future. This is of course not surprising, data anxieties, as Pink et al. (2018) have shown, are often interconnected with the impossibility of knowing the future (p. 2). In addition to that, as Livingstone and Sefton-Green (2016: 213) have argued the future is an ontological and imaginary construct that plays a fundamental role in the relationship between adults and children. Young people and children are often negated the here and now and are referred to ‘what they may become’. Therefore, these data anxieties are revealing of how families are being affected by predictive time and how they are actively questioning the type of datafied societies that we are building.

One afternoon, for instance, I had the pleasure to sit down for an interview in a crowded Starbucks café near Angel Tube station in London with Caty, who worked as a civil servant and had a son who was 6 years old at the time. I had randomly met Caty 10 years before through a mutual interest, and we became Facebook friends then. We had never seen each other again since 2007, but I witnessed the birth of her child and followed him growing up on Facebook. It is for this reason that I asked her to participate in the project. As civil servant, Caty was dramatically aware of all the different changes in dataveillance and data governance, and she was particularly critical and concerned for her son’s future because of the introduction of the 2016 Investigatory Powers Act in the United Kingdom (nicknamed the ‘snooper charter’) a legislation act that expanded the electronic surveillance powers of the UK intelligence system and the police. When she thought about the ways in which dataveillance and predictive analytics would impact on her son, she said,

The snooper charter feels me with dread. I don’t really think that there are malignant aims, but I know that there is a lot of incompetence, a lot of manipulation to access your data. They use the data for racial profiling and other serious issues. There seems to be constant justification of further intrusion.

Caty seemed to be very concerned about the future and the impact of current data traces, and then added jokingly, ‘Hopefully, my son will become a nice hacker and will be able to hide’.

During the research project, many parents like Caty were actively reflecting and negotiating with predictive time, especially by looking into the future. Yet what I realized through my project was the fact that parents often did not grasp the extent of dataveillance. Few were aware of the latest technological developments in facial recognition (Gates, 2011), educational profiling (Williamson, 2016), genetic discrimination (Goodman, 2016) or predictive policing (Dencik et al., 2018). They also had not really grasped the fact that – as I show in detail in my new book (Barassi, in press) – through everyday technological use children are being datafied from before birth, and this is enabling surveillance capitalism to track citizens, govern and discipline them throughout a life-time.