The often discussed ‘digital divide’ (Van Dijk 2020) between those who can benefit from the digital age, and those who cannot, is generally perceived as an issue of access to infrastructure and digital devices. When followed by an emphasis on digital skills training for those who have been disadvantaged, exactly how individuals encounter, create, or respond to data, or indeed how they resist the collection of data about themselves, is rarely mentioned. For example, a recent UK House of Lords report, ‘Beyond Digital: Planning for a Hybrid World’ (Marston et al. 2021), examines the complex implications of the Covid-19 pandemic on economic and social wellbeing. Whilst human digital interactions are acknowledged in the report as diverse, hybrid, or postdigital in nature, data tends to be mentioned in relation to research and statistical variation of access to services, or with regard to cyberattacks, or general loss of data. Yet data takes many different forms in postdigital society, and all of these are relevant, in matters of digital inequality and disadvantage.
In our forthcoming Springer book,Footnote 1 what we think of as ‘data’, and what it means for each of us to be recorded by it, to generate it and to interact with it, is shown to be a crucial part of any cross-sector debate concerning digital inclusion. This is particularly important too given the intimate relations that data-driven technologies, Artificial Intelligence (AI), the Internet of Things (IoT), algorithmic culture, facial recognition systems (Ada Lovelace Institute 2019), and wearable technologies have now assumed in our lives and learning, either with or without, our consent. Whether this concerns software code, data analytics, social media interactions, or other infrastructures involving data of some sort, these have ‘become inseparable from policy processes and modes of governance’ (Williamson 2019, 2020). During the Covid-19 pandemic lockdowns of 2020/2021, much additional online activity, tracking systems and applications, have added extra complexity too. In many policies though, facts and figures concerning matters of digital inclusion may be referred to as data, but they do not illuminate the complex forms that data now takes. Nor do they address the diverse and unequal ways in which people have capacity to interact with, or understand their relations with, data.
Having identified these issues, there is no quick remedy for one group of scholars, digital entrepreneurs, charitable agencies, or policy makers to address them alone. Research into the automation of digital poverty management has already demonstrated frightening, life threatening impacts for the vulnerable, from algorithmic decisions in data-driven eligibility systems and predictive models, to poor privacy and data security, or infringement of rights via surveillance (Eubanks 2018: 11). The ‘digital poorhouse’ described by Eubanks refers to the automation of decision making about access to services for the disadvantaged in society, where shared social decisions instead become ‘systems engineering problems’ (Eubanks 2018: 12). Yet even in discussing powerful analogies between public assistance programmes that have moved ‘from poorhouse to database’ (Eubanks 2018: 14), the emphasis is on how data gathered on people’s circumstances is being managed. How each of us are managing data ourselves, to interact with it in every shape and form, remains under-explored.
The relatively new field of Human-Data Interaction (HDI) was proposed in order to ‘open up a dialogue amongst interested parties in the personal and big data ecosystems’ (Mortier et al. 2014). Intended to offer a framework for more meaningful relationships with data, it was designed by these authors primarily to guide the practices of those who are developing data-intensive systems. Comprising of three key tenets: agency, legibility, negotiability, the HDI framework is intended to be sensitive to the socially situated nature of data and data-driven systems (Mortier et. al. 2014). The theme of resistance was recently introduced too for additional exploration. There are though some issues to consider, if HDI is to be helpful in agendas aimed at addressing the digital divide.
Firstly, whilst the HDI framework has challenged technology designers to confront the task of building more ethical systems, the key tenets within the framework still originate from a largely Computer Science disciplinary focus. We perceive a need to broaden the disciplines that engage with HDI and to bring their contributions into dialogue with agencies in the community that are working with disadvantaged individuals to address digital inclusion. Secondly, there can be an expectation that if a theory-led disciplinary framework exists, then this simply informs the direction of future policy and practice concerning data-driven systems. This though tends to overlook diverse ‘postdigital positionalities’ (Hayes 2021) of individuals and their contexts in a top-down approach. An alternative way to view human-data relations is to expand the HDI framework via real grassroots examples. This bottom-up approach disrupts a systems engineering focus alone on data management, with a view instead towards data empowerment.
In the sections to come, we explore how such an approach might be developed. Initially, we provide some examples of the many expressions that data now seems to take in our lives, in order to illustrate just how elusive, the term itself can be. It is important, if we are to expand ideas about HDI, that we ask the question: In human-data interaction, what exactly are we interacting with? This opens a debate that is much broader than fairness of access to services through computer systems that gather our data, and algorithms that decide who gets what. It is a debate that concerns a larger ‘postdigital dialogue’ (Jandrić et. al 2019) in the community about how humans recognise and interact with the data they are creating in physical and virtual spaces, as well as their rights in relation to data created about them. We therefore raise the topic of enabling cross-sector environments, where such debate might take place, in order to further postdigital inclusion.
Whilst much is currently discussed concerning digital inclusion, to increase the access of citizens to digital services and devices, postdigital inclusion expands these arguments to take into account the messy, hybrid lives of postdigital citizens. Data does not sit neatly in spreadsheets and computer systems alone, it is a shadowy actor in our lives wherever we go, regardless of whether we use digital devices or not. Expanding the existing HDI framework with rich, diverse, cross-sector opinions and interdisciplinary theory is one way to further postdigital inclusion. It also demonstrates a form of postdigital knowledge exchange in action that informs policy based on activities in local communities. It is not often that both powerful, cross-sector activity and interdisciplinary academic debate take place together, to co-inform theoretical frameworks. Yet this is crucial now, given that across all disciplines there are critical, emerging (and sometimes converging) issues surrounding humans and the data they generate directly, or that is generated in relation to them, and their activities.
We conclude therefore that taking a conceptual framework and simply utilising it inform policy and/or practice is not enough in the case of human interactions with data. This assumes the theoretical framework is being applied to something static. What is needed instead are new ways to capture what is being learned in communities where the role of data in the digital divide is being addressed in ways that empower individuals. Finding routes to channel this cross-sector learning back into developing the HDI framework acknowledges the complexity of the data ecosystem, as we seek to collectively further postdigital inclusion.