Abstract
Welfare
The Covid-19 pandemic has re-actualized calls for a strong state to provide welfare for all. This included firm measures to fight the spread of the disease and financial support for those strongly affected by the disease and consequent restrictions. The pandemic also made the efforts of workers within critical infrastructures visible: the nurses and doctors, the home delivery couriers, the supermarket employees, and the workforce in public transport as well as teachers and librarians. They continued to toil either like before the pandemic or had to radically adjust their daily tasks to ensure the welfare for the majority in society. Moreover, with strained public funds and an ensuing economic downturn, questions arose of what constitutes essential welfare, who should be entitled to receive support, and which tasks should be left to administrative or civic initiatives, respectively.
The question of what belongs to welfare systems and how they could be sustained during the pandemic went hand in hand with new processes of state-induced datafication and surveillance with the help of contact tracing apps and symptom trackers. 1 Not only have we seen new applications that datafy health status and contact networks, but the pandemic has also led to an acceleration of digitalization efforts in other domains including schools, culture, and public agencies. With these developments in mind, it seems apt to revisit the concept of welfare and challenge the long-term implications of datafication and digitalization for the welfare state. And much is at stake. Welfare provision is fundamentally about how societies are organized and what shared values prevail to guide this provision, but also what lives people are able to live.
In this crosscurrent contribution, we approach the notion of welfare through the lens of the data welfare state. We, further, suggest that datafied welfare can be fruitfully studied with the capabilities approach (Nussbaum, 2003; Sen, 1999) to better understand how ideas and values of data welfare intersect with and may allow for the ‘good’ life and human flourishing. The main aim is to highlight the deep-seated changes of the welfare state that emerge with the delegation of care and control tasks to algorithmic systems and the automation based on datafication practices. Welfare provision is undergoing major shifts that imply fundamentally rethinking the role of technology that supports and enhances welfare with the help of data.
Welfare and technologies
Welfare is a keyword of modern society. According to Williams (1976), it was originally used to indicate happiness or prosperity. The notion of organized welfare through institutionalized provision for basic needs emerged first in the 20th century and the idea of the welfare state was first mentioned during the Second World War in 1939. It is built on normative ideas of universalism, equality and decommodification (Jakobsson et al., 2022). Based on these principles, arguments for the welfare state are put forward such as its ability to enhance social cohesion, as well as a balancing of risks while preserving human dignity (Jakobsson et al., 2022). Part of these arguments have addressed the media and technologies that should either foster welfare provision or are deemed part of the pre-conditions to organized and institutionalized welfare, for example public service media (Nikunen and Hokka, 2020). Some even placed the media at the heart of welfare and Mjos et al. (2014) capture in the notion of the media welfare state. With digitalization and datafication, scholars have increasingly engaged with the shifts in the conditions and possibilities of welfare provision. For example, Lina Dencik argues that the datafied welfare state is based on two logics, first an actuarial logic that individualizes risks of social problems, and second a logic of rentierism which refers to the underlying economic model that reinforces the production and circulation of data. This, she argues, leads to a re-configuration of social powers undergirding the welfare state. We, following sociologist Fourcade (2021), would add to that a re-configuration of the relationship – the social contract – between the citizen and the welfare state that is increasingly mediated by digital data and technologies for algorithmic automation.
How welfare and different kinds of media technologies are intermingled and related to each other has been studied from a broad range of perspectives. The approaches can broadly be distinguished into three interrelated fields studying the nexus of digital media technology and welfare: first studies of the media welfare state and its extensions focusing on the data(fied) welfare state, second examination of the digitalization of the welfare state, and third work on welfare technologies.
The first subfield emerges within media and communication studies. Here, the notion of the media welfare state focuses on the way the media system can facilitate communication to live a good life (Mjos et al., 2014). Media policy promoting a strong public service, for example in the Nordic countries, has been of particular importance here. On this note, Jakobsson et al. (2022) have lately turned to the normative foundations and motivations of media welfare. Furthermore, investigations and conceptualizations of the media welfare state explore whether and how the premises of media welfare are still valid in a digital media ecology (Ala-Fossi, 2020; Enli and Syvertsen, 2020; Flensburg, 2021). For instance, Flensburg (2021) argues that the increasing commercialization of internet infrastructure implies tectonic shifts in the institutional logics guiding the development of the welfare state and its provision of welfare. Relatedly, scholars have started to engage with the question of how processes of datafication are reshuffling the organization of the welfare state and the idea of welfare itself and its specificities in the Nordic countries (Andreassen et al., 2021; Dencik, 2022; Dencik and Kaun, 2020). These explorations extend earlier work on media welfare as they consider the consequences of datafication for the broader welfare sector and include studies of for example social scoring mechanism and automated decision-making in public administration (Kaun, 2022).
The second strand emerges out of sociology with a focus on welfare policy. Here, a growing research field is taking issue with broader trends in the digitalization of the welfare state (Busemeyer, 2022). Special emphasis is put on welfare policies to meet challenges of platform economies and increased automation that influence the development of the job market. Moreover, it raises questions about how digitalization, datafication and the metric logics underpinning it might altogether change the relationship between the state and its citizens (Fourcade, 2021).
Lastly, the field of welfare technologies focuses on devices that are employed primarily in the care sector including care robots and sensors in the home of receivers of care (Kamp et al., 2019), and of people’s experiences of technology-supported care (Langstrup, 2013). This field emerges out of science and technology studies as well as other technology-oriented disciplines including human computer interaction.
The emergence of the data welfare state
Out of these different fields of engaging with the technology and welfare nexus, one notion emerges as particularly relevant to rethinking the welfare state in post-pandemic times, the data welfare state. The data welfare state that refers to the increased reliance on digital data for decision-making and welfare provision is an extension and continuation of earlier tendencies including the very roots of the certain welfare state models. In particular, the social democratic welfare model with universal access as a foundation, was motivated by and organized around scientific knowledge and information. Ideas of social engineering were based on statistics and large population registries. The data welfare state of today with digitalization as a pre-condition extends these earlier arguments but is often embedded in a shift toward managerial arguments of cost efficiency and quick service delivery rather than universal access to welfare and an idea of progressive society.
In many ways, the ideal of the data welfare state follows the normative foundations of the media welfare state that put media at the center and heart of welfare. Both ideal models are based on universalism, equality and decommodification motivated with arguments on social cohesion, risk exposure (externalization of risks) and human dignity (Jakobsson et al., 2022). Universalism refers to the general access to welfare programs, that is, all members of a specific community have access to welfare, while in the selective model community members have to qualify for access (Jakobsson et al., 2022). The welfare state should however not only guarantee universal access but also actively foster equality. This ought to be reached by services that are decommodified and do not follow the market logic.
To conceptualize the data welfare state, Andreassen et al. (2021) develop and reformulate the ideals of the media welfare state that encompass four pillars according to Mjos et al. (2014), namely, universal access, editorial freedom, content diversity and durable policy solutions to safeguard the media welfare state. Based on this, Andreassen et al. (2021) suggest considering as key pillars of the data welfare state ‘1) justice and non-bias in processes of datafication; 2) decommodification, that is, freedom from commercial logic; 3) data diversity acknowledging different needs of citizens and residents; and 4) transparency on the datafication process providing sustainable and meaningful information for citizens and residents’ (p. 210; see Table 1).
Adaption of four pillars of the media welfare state to the data welfare state.
Comments: the pillars of the media welfare state were formulated by Mjos et al. (2014).
Source: Andreassen et al. (2021: 210).
By shifting attention from (the distribution and regulation of) media messages to datafying processes, the notion of the data welfare state can be seen as a framework that highlights how new structural conditions such as the digital infrastructures for tracking and datafying citizens and automating welfare decision-making change state-citizen relations and public values. We suggest studying these changing relationships through the lens of the capabilities approach. While there is a shift toward increased datafication partly facilitated by the pandemic and measures to decrease the virus spread, the media and data welfare endure as parallel ideals that co-exist simultaneously. At the same time, the data welfare state addresses a new formation of organizing and administrating society and begs the question how the ideal of welfare as human flourishing is reached. This question becomes especially urgent in the context of limited resources and rising needs. The pandemic has pushed these fundamental questions to the front; questions such as how should the majority of society behave to safe guard vulnerable groups, are there hierarchies between needs and how should emerging inequalities in the pandemic situation – not only a health but also moral and societal crises – be addressed, emerged and how all of these challenges should and can be met with data-based technologies.
Human flourishing and capabilities approach
Crisis contexts such as the pandemic make clear that different groups in society need different resources to flourish. The need for diverse responses and forms of support to contribute to human flourishing has earlier been addressed under the umbrella of the capabilities approach (Nussbaum, 2003). Building on Amartya Sen’s pioneering work in welfare and development economics, Martha Nussbaum (2003) suggested that justice concerns ‘what people are able to do’, that is, their capabilities to actually achieve well-being rather than formal rights or freedom. This implies, she adds, that ‘individuals need different levels of resources if they are to come to the same level of capability function. They also have different abilities to convert resources into actual functioning’ (p. 35). In essence, the capabilities approach shifts attention from universalist, predetermined understandings of what a good life is, and instead addresses human flourishing by assessing in empirical, concrete contexts the resources and choices available to various individuals and groups to pursue and lead the lives they have reasons to aspire for. The capabilities approach has recently been taken up in media and communication research, most notably in theories about communication, justice, and ethics (Couldry, 2019; Jensen, 2021), and studies of digital inequality (Kleine, 2013).
The capabilities approach informs the notion of welfare by stressing the complexity of lived experience and the diverse values that human actors may orient and attune to in seeking to lead their life. What is the good, then, cannot be determined in absolute or utilitarian terms by stressing for example that the good means that what is efficient, one of the common justifiers for any data-driven development in the data welfare state today. Instead, welfare and the values underpinning it must be understood and studied in concrete relation to the people and organizations it implicates in respect and care for diversity, equity and justice. This necessarily includes comparative approaches across welfare domains ranging from social benefits to child welfare and public safety to communal welfare infrastructures in smart city initiatives, but also across national contexts and diverse welfare regimes. Only through comparative approaches we can develop an in-depth and contextualized understanding of the data welfare state.
Arguably, in capability terms, the role of the data welfare state is to provide the basic support and resources people need in pursuing the lives they have reason to value. While at the same time, support should differentiate between different needs and presuppositions for flourishing. As data-based automation is strongly based and focused on streamlining and standardization, the nuanced and differentiated capabilities approach to welfare needs special emphasis. We need to imagine welfare technologies that allow for different capabilities and acknowledge diverse needs. During the pandemic however societies experienced rapid changes with crucial implications for fundamental values. For example, the idea of separating infected from healthy bodies facilitated by digital solutions pushed an acceptance of datafication in unimagined scale. In this and many other ways data became a proxy for the ability to provide welfare and form of mediation of the social good. Hence it is urgent that media and communication scholars turn to the question of how the data welfare state can and should foster capabilities for human flourishing. A question that has to be at the heart and not the margins of the discipline.
Footnotes
Funding
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