What ifs: The role of imagining in people’s reflections on data uses

Introduction

This paper explores imagining as a practice which takes place when people reflect on data uses, a shorthand phrase we use to refer to data collection, analysis and sharing. In recent years, research into what people think and feel about data uses has proliferated (see Kennedy et al., 2020 for a summary), because understanding public views of data uses is considered to be essential for ensuring that data works ‘for people and society’ (the mission of the Ada Lovelace Institute in the UK). However, such uses are complex and explanations can be long, hard to follow, or not available at all. This makes understanding how people reflect on and arrive at thoughts and feelings about data uses important. It is therefore crucial to take a step back from what people think and feel about data uses, to consider how they do their thinking and feeling, yet this rarely happens.

Reflectivity is understood as the way individuals deliberate ‘on particular issues as a means of sorting complex and ill structured thoughts, perceptions and ideas’ (Malthouse et al., 2014:599). As such, it enables enquiry into thoughts, feelings and beliefs (Bolton and Delderfield, 2018:13). In this paper, we explore how people who do not identify as data experts (hereafter referred to as non-experts for expediency) reflect on data uses when they are asked what they think and how they feel about them. We draw on interviews and focus groups with diverse members of the UK public (n = 112) in which we explored their perceptions of specific public sector data uses, a phrase we used to refer to data collection, analysis and sharing. We focused on data uses rather than just data, because uses of data matter. It is now well-established that the purposes for which data are used have effects, benefitting some and disadvantaging others, in contexts ranging from targeted advertising (Turow, 2012) to child welfare (Heikkilã, 2022) and criminal justice (Angwin et al., 2016). Furthermore, what people think and feel about data depends on whose data is collected, how and where it is collected, for what purposes and with what consequences (Kennedy et al., 2015). In other words, uses of data and the contexts in which they take place shape people’s reflections. We therefore explored reflections on data uses in their infrastructural contexts.

In the paper, we argue that imagining is one way in which reflection on data uses happens. We use imagining to mean building or creating a mental image of something that is not present in the moment of imagining. We found that participants imagined by asking themselves ‘what if’. What if, in the future, data is used for a different purpose or by different people than originally stated or intended? What if the socio-political context changes and this has consequences for data uses? What if people from more disadvantaged or minority groups are excluded from some data uses? Although highlighting the role of imagining in relation to technology and data is not new, much existing scholarship is about ‘imaginaries’ not ‘imagining’ (see Jasanoff and Kim, 2009; Taylor, 2002; ). Like literature on attitudes to data uses, work on imaginaries tends to focus on what is being imagined. Adopting the terminology of ‘imagining’ rather than ‘imaginaries’, this paper focuses instead on ‘the technologies of imagination’ (Sneath et al., 2009), or how imaginings are generated and the ways in which they work in practice at the level of the individual.

In literature that explores imagining in relation to data uses, imagining is often positioned as something people do to fill a gap in knowledge, information or understanding (see Bucher, 2017; Markham 2021; Oman 2021, 2022). We challenge this argument, demonstrating that imagining is a reflective practice that takes place when these three things are present. As we describe below, in our research we provided participants with information, in the form of visualisations, about specific public sector data uses, thus enabling them to know something about these data uses when discussing their thoughts and feelings about them. And yet, participants still imagined and asked themselves ‘what if’. We therefore argue that imagining is not a substitute for knowledge, information, understanding. Rather, it is entangled with them.

Overall, this paper contributes empirical insights into how reflectivity takes place when non-experts talk about what they think and how they feel about data uses. We argue that imagining as a reflective practice unfolds through (1) imagined scenarios and (2) locating the self and others, and that it shapes participants’ thoughts and feelings about public sector data uses. Below, we map out the debates in which we situate our research. We then outline our methods, and proceed to elaborate our argument about imagining as a reflective practice.

Understanding reflectivity and imagining

According to Malthouse et al., reflectivity refers to individuals ‘deliberating on particular issues as a means of sorting complex and ill structured thoughts, perceptions and ideas’ (2014: 599). Reflectivity ‘can enable inquiry into what you think, feel, believe, value’ and work to ‘make things more comprehensible’, according to Bolton and Delderfield (2018: 13). We build on these definitions of reflectivity in this paper, linking them to our understanding of imagining as the practice of building or creating a mental image of something that is not present in the moment of imagining. Drawing on Kant’s proposition that ‘the imagination is pervasive in all human apprehension’ (see Sneath et al., 2009: 6), we approach imagining as something that is also connected to the process of ‘making things more comprehensible’, or of coming-to-understand. We see reflectivity and imagining as connected but neither the same nor interchangeable. Imagining, or the building of a mental image, is one way in which people reflect – that is deliberate, think through and consider – on issues. Imagining is therefore a practice which forms part of reflective processes.

In literature that explores imagining in relation to data uses, imagining is often positioned as something people do when there is a gap in knowledge or when they do not know something. Markham, for example, defines an imaginary as ‘what emerges as a person speculates about something they do not know’ (2021: 385). Similarly, Bucher (2017), argues that participants in her study of Facebook users’ perceptions of the platform’s algorithm filled an explicitly stated gap in their knowledge about how the algorithm works with their ‘algorithmic imaginary’, defined as ‘elaborate theories about what the algorithm is and ought to be’ (2017: 40). Although algorithms are not data, they inform how data is used and the infrastructural context in which uses take place. A final example is research by Oman (2021 and 2022), who found that, in order to make sense of data-in-context, people imagine when they do not necessarily know all that they need or want to know.

In this paper, we challenge the idea that imagining replaces knowledge. We provided our participants with information on the public sector data uses we talk to them about in the form of visualisations detailing how data is collected, how it is analysed and how it is shared. Unlike in Bucher’s work (2017), our participants were given the opportunity to know something about the data uses we discussed with them. We show how and in what ways imagining presents itself when information about data uses is made available.

Connecting (reflective) imagining with data literacy and agency

With data uses being difficult for non-experts to define and understand, data literacy, that is the ability to know, understand and have skills in data use and management, is of increasing importance (Pangrazio and Selwyn, 2019). Data literacy has been approached in numerous ways with emphasis often placed on technical skills and ability (see Calzada-Prado and Marzal, 2013; Carlson et al., 2011). Communication scholars, though, have tended to follow the ‘new literacy studies approach’ (Gee, 2000; Street 1994) expanding what counts as literacy to include the process of developing critical understanding. This has led scholars such as Pangrazio and Selwyn (2019) to develop specific frameworks for approaching literacy in the context of data, something they name ‘personal data literacies’. This type of literacy is not just about technical skill, it’s about building an awareness of ‘social, political, economic and cultural implications of data’ and the creation of a space to ‘reflect critically on these processes’ (Pangrazio and Selwyn, 2019:426). In fact, personal data literacy ‘requires critical reflexivity regarding the implications of data’ (emphasis added, ibid) with Pangrazio and Selwyn naming data reflexivity as one of five domains of personal data literacy. In this way, reflectivity (or reflexivity as it is referred to in their work) is framed as an integral part of the process of coming to know and understand data.

Our paper builds on work on personal data literacies by exploring how non-experts engage in the process of ‘data reflexivity’ when they talk about data uses. Specifically, we argue that imagining plays an important role in this reflective process. We also work to connect our findings to debates on data agency. Greater data literacy has been linked to individuals having greater agency (Kennedy and Moss, 2015). This is not agency in the sense of ‘brute force’ but instead the kind of agency proposed by Couldry (2014 and applied by others e.g. Kennedy and Moss, 2015) of an ‘an elongated process of action and reflection on the sociocultural context of a practice or text’ (Pangrazio and Selwyn, 2019:427) allowing individuals to ‘give an account of what one has done, even more basically, making sense of the world so as to act within in’ (Couldry, 2014: 891). We argue that imagining plays an agentic role in that it facilitates reflectivity, which in turn facilitates data literacy, paving a path for individual agency.

Unlike Pangrazio and Selwyn (2019), we choose to use the term reflectivity rather than reflexivity. Following other scholars, we see reflectivity as a practice in which individuals consider events or situations, look at scenarios from different angles and work through what they think and feel (Bolton and Delderfield, 2018). According to commentators, reflexivity entails an individual positioning ‘himself or herself within the broader social and organisational causes of particular problems’ (Malthouse et al., 2014: 599) to consider the effects of their own actions on social structures (Bolton and Delderfield, 2018). The term reflectivity more appropriately captures what we found in our empirical data, where participants focused on the real and imagined effects of the data uses rather than the effects of their own actions.

Distinguishing imaginaries from imaginings

Imagining in relation to technologies of all kinds, including data uses, is often discussed through the concept of ‘imaginaries’ (see Jasanoff and Kim, 2009; Taylor, 2002; ). In this literature, two dominant understandings are mobilised: the first focuses on social imaginaries, the second on socio-technical imaginaries. The social imaginary is described by Taylor as ‘a kind of common understanding which enables us to carry out the collective practices that make up our social life’ (2002: 106). For Taylor, social imaginaries are both ‘factual and normative’. That is, we have a sense of how things usually go, but this is interwoven with an idea of how they ought to go (2002: 106). The social imaginary is ‘about the way ordinary people imagine their social surroundings, which is often not expressed in theoretical terms but is, instead, carried in images, stories, legends, and so on’ (Taylor, 2002: 119).

Socio-technical imaginaries are described by Jasanoff and Kim as analytical devices that capture the ‘collectively imagined forms of social life and social order reflected in the design and fulfilment of nation-specific scientific and/or technological projects’ (2009: 120). Socio-technical imaginaries scholarship tends to focus on how powerful actors such as nation states, governmental actors and public institutions envision and enact technoscientific developments (Jasanoff and Kim, 2009), although the range of actors whose socio-technical imaginaries are studied in this field has expanded more recently to include corporate actors, civil society, research communities and other organised groups (Mager and Katzenbach, 2021). Socio-technical imaginaries are often seen to imagine desirable technological futures. As such, they are ‘at once descriptive of attainable futures and prescriptive of the kinds of futures that ought to be attained’ (Jasanoff et al., 2007: 2). They are future-oriented both in their prescriptiveness and because they are seen to be performative, bringing into being that which they prescribe.

Social and socio-technical imaginaries are both seen as ‘a kind of common understanding’ (Taylor, 2002: 106) and as ‘collective’ in nature (Jasanoff and Kim, 2009). Social and socio-technical imaginaries literature tends to focus on what these shared imaginaries are. As such, imaginaries are outcomes, rather than practices or processes, and they are collective, rather than individual. Sneath et al. (2009) are critical of understandings of imaginaries as collective, arguing that they enlarge the concept such that it becomes used in similar ways to notions such as ideology and culture (see Strauss, 2006). They argue that this leaves little room for understanding how imagining enables meaning to take shape. To address this problem, Sneath et al. (2009) argue for a focus on the ‘technologies of the imagination’, a term they use to refer to the means by which imaginings are generated. Following Sneath et al., we focus on how the practice of imagining unfolds as part of a reflective process when people talk about data uses. Rather than focussing on the ‘prescriptive’ outcomes (like work on socio-technical imaginaries does) we argue that it is important to focus on the ‘technologies of the imagination’ due to the prescriptive, or determining, role they play in how people ultimately perceive reality (Sneath et al., 2009). We understand imagining as ‘insinuated into perception itself’ (Crapanzano, 2004: 19). Understood in this way, imagining has the potential to shape how people think and come to have feelings about data uses. Shifting the focus from collective imaginaries to individual imaginings, we use the term imagining rather than imaginaries in the remainder of this paper.

Although we focus on individual imaginings, we are not arguing that such practices take place in a social or political void. We understand imaginings as socially constructed and acknowledge that, like imaginaries, they are shaped by their particular socio-political contexts (see Jasanoff and Kim, 2009). As Markham (2021) puts it, they ‘borrow from what is already known or supposed about the world’ (pg. 385) and as such they ‘reproduce current ideological trends’ (pg. 384). We also recognise that collective imaginaries about data and the social world more broadly inform reflective imaginings. We acknowledge the socially constructed character of imaginings by exploring how different forms of everyday knowledge, experience and identity shape talk about data uses and what people imagine about the ways in which data are used.

Methods

We carried out empirical research in the UK via interviews and focus groups with 112 participants and a survey (n = 2000). This paper focuses on the data collected via interviews and focus groups where we saw imaginings play a role in participants’ reflections on data uses (see Taylor et al., 2023for details on the quantitative element of this research). As our research took place during the COVID-19 pandemic, our qualitative research was conducted either online (via video call) or over the phone. Because we were interested in the role that inequality plays in perceptions of data uses, we recruited 112 demographically diverse participants. 85% were from one and 46% were from two or more disadvantaged groups. Often this meant participants were from minority groups in the UK (e.g. LGBTQ + participants, racially minoritized participants), but not always (e.g. female participants). We reflect on how our participants’ demographics and their perceptions of data uses in an alternative paper (Kennedy et al., 2024). We received ethical approval from our university prior to carrying out our research.

We researched perceptions of data uses in public sector organisations in the UK, focussing on people’s views of specific data uses in specific domains. We did this because previous research had asked questions about data uses in abstract and general terms (Big Brother Watch, 2015; The Direct Marketing Association, 2018), which limits understanding of public perceptions of actual data uses. Aware that much of the previous research into public attitudes to data uses had focused on attitudes to high-profile data practices (e.g. Dencik and Cable 2017), we also felt that it was important to understand people’s views of less high-profile and more everyday data uses like those that take place in the public sector, because of the role that they play in shaping everyday lives. It’s worth noting, that because of their less high-profile nature, participants were less likely to have prior knowledge of the data uses shown to them. We focused on six data uses in the government Department for Work and Pensions (DWP), the British Broadcasting Corporation (BBC) and the National Health Service (NHS), because these organisations will be familiar to many UK residents and they cover core aspects of everyday life: welfare, media and health. We partnered with the DWP and BBC to identify data in the first two domains. This meant that our contacts in these organisations selected the data uses on which our research focused. For the latter, health, we drew on publicly available information and prior research. The two DWP data-based systems focused on ways of making it possible to verify identity online, BBC data uses focused on personal control over data and NHS data uses focused on data sharing and re-use.

We wanted to ensure that participants were informed about the data uses we discussed with them, because we think that people need to know what a data use involves in order to develop thoughts and feelings about it. And yet, research on public perceptions of data uses often does not seek to establish what people know or understand prior to asking them their views, or it does not enable such understanding before asking about thoughts and feelings (e.g. Humphreys, 2011; Young and Quan-Haase, 2013). We addressed this issue in our research by using elicitation and engagement techniques to show and explain data uses to our participants. We drew on ‘data journeys’ approaches (Bates et al., 2016), developing detailed knowledge of each data use via interviews within the organisations responsible for them and analysis of available public documentation. We produced written accounts of the data uses, of which a professional data visualiser then produced visualisations of them. As critical scholars of data uses, we acknowledge that it is not possible to produce objective accounts of data uses, because interpretation takes place in the act of describing, illustrating and choosing what to highlight (Ditchfield et al., 2022). We know that methods shape findings, that ‘they have effects; they make differences; they enact realities’ (Law and Urry 2004, p. g. 392-3).

In interviews and focus groups, we presented these visualisations to participants (see examples of visualisations in Figures 1–3 in the analysis) and asked them what they thought and felt about the way data was being used. Whilst knowing it was important to provide information on data uses before asking participant’s opinions, we are aware that providing information to our participants about data uses may have encouraged them to think about them in ways that they would not have otherwise. We aimed to find a balance between informing our participants of data uses and their potential effects and not leading them to conclusions. We sought to do this by describing and visualising data uses in ways which were clear, accurate and balanced – for example, we sourced information from experts other than our partners, such as civil society data advocacy groups. We began the interviews and focus groups by asking participants about their awareness of the ways in which personal data is used, their perceptions of benefits and concerns and whether they had acted on any concerns they had. We then showed them visualisations of two or three of our public sector data uses, asked them to describe what they saw, and added details as needed. We asked broad, open questions such as ‘What do you think of this data use?’ to create space for participant-led discussion (see Ditchfield et al., 2022 for extended reflection on these methodological challenges). Through this approach, participants knew something about how our selected data uses work and yet, as we demonstrate below, they still imagined ‘what ifs’. Our methods thus enabled us to advance understanding of the relationship between imagining, knowledge and information in people’s perceptions of data uses.OPEN IN VIEWER

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Figure 2.

DWP Dynamic Trust Hub, a project exploring ways to enhance identity verification.

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Figure 3.

DWP Confirm Your Identity, an identity verification process for welfare payments which makes it possible to confirm identity online.

We carried out thematic analysis of the qualitative data, grouping codes into themes deductively, based on our research questions, and inductively, based on what emerged from the data. Multiple coders discussed the same transcripts to ensure codes were being used in as standardised a way as is possible with qualitative research data (Silverman, 2011). Although the empirical focus of our research was on what people thought about these public sector data uses, the semi-structured character of our interviews and focus groups allowed us to access the reflective practices that our participants engaged in when attempting to make sense of the data uses presented to them. One such reflective practice was imagining. In our analysis, we discuss two ways in which imagining unfolded: through imagined scenarios and locating the self and others.

Imagining as a reflective practice

Discussion and conclusion

Our analysis shows that the practice of imagining forms part of the reflective process that takes place when non-experts talk about data uses. We argue that imagining is a reflective practice that aids individuals in deliberation and the sorting of thoughts and as such, it enables access to what people think and feel about data uses. Our work contributes to literature on imagining and imaginaries by advancing understanding of how imagining happens at an individual level. Rather than showing the ‘prescriptive’ role imaginings can play in ‘co-produce(ing) the very future they envision’ (Mager and Katzenbach, 2021: 224), we have shown the determining role that imagining can play in shaping how people perceive public sector data uses (Sneath et al., 2009).

Imagining in relation to data uses is often perceived as something people do when there is a gap in knowledge (see Bucher, 2017; Markham, 2021; Oman, 2021, 2022). However, we provided participants with information about specific data uses and spent time in interviews and focus groups discussing and explaining them. Yet, our participants still imagined ‘what ifs’. They asked themselves: what if the data is used in a different way, the context changes, you belong to a group which is disadvantaged by a data use? In our research, imaginings happened in the presence of knowledge, not in its absence. Our participants used the information we gave them as a springboard to imagine which helped participants think through and come to understand data uses.

Our research therefore advances understanding of the complicated relationship between knowledge and imagining. Imagining can be centred on things that are unknown, but it also exists in a complex entanglement with what is known. In our fieldwork, knowledge fed into and was entangled with our participants’ reflective process of imagining, for example, when Ellis drew on their current affairs knowledge that Greenpeace was on the UK government’s counter terrorism list, to inform his imagining of how BBC’s Own It may be used in different socio-political contexts. Participants’ imaginings were entangled with experiential knowledge, such as Tahira’s experience of charity work and of connection to marginalised communities. Knowledge also derived from experiences of engaging with similar data systems to those we showed our participants, such as Rosie and Kerry’s experiences of using the UK Government Gateway system. Experiential knowledge deriving from participants’ identities also fed into their reflective process of imagining. Lousia’s imagining of the possible future uses of Own it data was informed through her identity as a mother. Rosie and Kerry highlighted their identities as well-educated people, when imagining the experiences of other users less well-educated than they were. Ellis imagined possible consequences of Own It data use for the LGBTQ+ community, to which they belonged.

Whilst many forms of knowledge on which participants drew in their imaginings about data uses were experiential and everyday, other forms of knowledge also played a role. We saw that broader imaginaries of social life informed our participants’ imagined scenarios, for example, when Kerry imagined a ‘single mum’ engaging with DWP systems. We also acknowledge that ideological trends and discourses may shape the imaginings that people have (Markham, 2021). High-profile data misuses and scandals (such as Cambridge Analytica, see Lapowsky, 2019) may inform participants’ imagining of dystopian future scenarios. However, this is not something we addressed in our research. Rather, our research illuminates how lived experience and identities produce knowledge which informs reflective imaginings about data uses, which in turn inform thoughts and feelings about and evaluations of them. Because participants’ imaginings shape their feelings about data uses, they have an impact on real life. At the same time, real life, in the form of experiential knowledge, also impacts what is imagined. In this way, different types of knowledge exist in ‘dialectical tension’ (Crapanzano, 2004: 15) with each other in reflective imaginings of data uses.

Our research shows that even when people are given information about data uses, their perceptions of these data uses are shaped by their various imaginings about them. Different, better, clearer information about data uses will not necessarily stop the reflective imagining process, because people still imagine in the presence of information. Nor would it be desirable to stop people imagining about data uses, we suggest. This is because imagining is an important component of reflectivity. It aids the reflective process, helping people to identify what they think and feel. This finding builds on scholarship on data literacies in that we identify imagining to play a significant role in the reflective process; a central domain of personal data literacies (Pangrazio and Selwyn, 2019). With data literacy argued to be connected to forms of agency (Kennedy and Moss, 2015) we therefore argue that imagining, plays an agentic role in that it facilitates reflectivity, which in turn facilitates data literacy, paving a path for individual agency. This is a different kind of agency to that seen in the work in Bucher (2017), where peoples’ algorithmic imaginaries are seen as agentic in terms of shaping how algorithms work. Instead, the data imaginings that we discuss in this paper are agentic in that they move people beyond a simple acceptance of data uses, to incorporate an everyday form of ‘critical questioning’ of the data uses shown to them (boyd and Crawford, 2012).

In this paper, we have approached reflection as a process of deliberation, of sorting thoughts, which enables access to thoughts, feelings, beliefs and values (see Bolton and Delderfield, 2018; Malthouse et al., 2014;). How data uses are experienced in everyday life is also reflected – in the sense of being mirrored – in what is ultimately imagined about data. In this way, we see a double reflective process unfolding when people talk about what they think and how they feel about data uses. Reflection happens in the deliberative sense and imagining is an important practice within this process. Reflection also happens in a mirroring sense of the term, with non-expert’s lived experiences and knowledge reflecting themselves in their imaginings. With this we see imaginings, in the context of talking about data uses, as something to be encouraged. We argue that they are an essential component of coming to understand and form views about data. Much as we aimed to find ‘the sweet spot’ in our methodological approach, we need to create spaces for people to engage in imagining as a reflective practice, given the agentic role it can play. We need to attend to imaginings, because of what they tell us about how people reflect on, come to understand and ultimately experience data uses in everyday life.