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Datafication in Medicine, Agriculture and Education

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Datafication in Medicine, Agriculture and Education The Promise of Precision: Datafication in Medicine, Agriculture and Education This paper analyses how precision has become a ubiquitous prefix in medicine, agriculture and education. The accompanying imagination in each of these domains is that ‘data’ will enable greater predictive accuracy through new sensors and interfaces. In this paper, we aim to provide insights regarding the ways in which precision assemblages function to augment and extend existing knowledge and data infrastructures, whilst also being underpinned by the anticipatory promise of the ubiquity of digital and sensing technologies. We argue that precision is marked by new data production and aggregation frameworks to measure and intervene. At the same time, precision draws on – and augments – established clinical, agricultural and educational subjectivities in ways that depict new logics of patient, student and environmental care. As we outline below, the threshold of the shift to precision is articulated and institutionalized at different points in each field we analyze in the subsequent sections of this paper – namely medicine, agriculture and education. This suggests that precision should be understood as an unevenly realized moment in policy development, rather than as simply produced through processes of technological change. Keywords: precision medicine, precision agriculture, precision education, data infrastructure, datafication, value Introduction ‘Precision’ has recently emerged as an increasingly common descriptor of specific domains of scientific and technological research and deployments in advanced industrial nations. 1 From the Obama-era Precision Medicine Initiative, to John Deere and Bayer-Monsanto’s promises of smart farming through precise fertilizer use, to ‘AttenivU’ tracking devices in classrooms, promises of optimised outcomes abound. The central claim across these domains is that new ‘precision’ technologies will add new forms of knowledge to, and further shape, data-led policy-making (Williamson 2019b). Contemporary promoters of precision medicine, agriculture and education promise policy-makers, owners of capital, students and other subjects unprecedented control over the manifold risks and opportunities that confront their institutions—hospitals, farms, classrooms. This paper sets out a preliminary attempt to compare the articulation of precision across diverse policy settings in medicine, education and agriculture. We argue that the promise and imagined value of precision is situated as both a critique of existing knowledge infrastructures and as an assertion of the emergent possibility of novel responses for new, ‘wicked’ or complex problems. Our objective in this paper is to compare three domains of vital importance to the management of populations, each with different historical relations to concepts of precision and attendant infrastructure. Precision is, somewhat ironically, a marker of displacement into a vaguely articulated future rather a signifier of present location. Proponents of precision in multiple policy domains predicate claim its value lies in both its future value as new associations are made visible through various automated analytics. This displacement into the future is key to the speculative political economy of data whereby assumed future use and exchange value is presented as the justification for gathering data in the present. At the same time, data is presented as highly mobile, and its sharing is a precondition for and feature of contemporary business models and policy rationales. In this sense, the emergent and fluid landscape of health, education and agricultural data that we discuss below constitutes a horizon of both political possibility and contestation (Lehtiniemi and Haapoja 2019). Articulations of precision in healthcare, education and agriculture are premised on the imagined value of data 2 infrastructures in producing novel outcomes in the context of existing policy settings. The curation of new data infrastructures in these domains is heralded as providing the conditions for new forms of knowledge, practice and evidence, and valorisation. Across numerous domains, the production of new data in ‘precision’ fields are accompanied by, or in some cases depend upon, the use of sensors which are claimed to increase certainty of knowledge and decision-making. All three domains – medicine, agriculture, and education – have been transformed by datafication: the translation of things such as brain activity, genes and soil chemicals into numbers (Mayer-Schönberger and Cukier 2013). Such translation is achieved by what Miles (2019), writing in the context of the development of precision agriculture systems, describes as an “epistemological relationship between industrialization and information” characterized by “algorithmic rationality, which … expresses the normative grammar of modern capitalist production” (p. 10). In a similar vein, Sharon (2018) argues that the ‘Googlization of health research’ constitutes a “new model of multi-stakeholder, data-driven health research that is emerging at the intersection of digital health and digital capitalism, and which must be situated in the broader context of the political economy of data sharing” (p. 10). Our concept of datafication emphasises the ways the generation of digital data, to be exploited through commercial analytics in the ways Miles and Sharon explicate, has itself become a rationale for policy-making. This rationale is epistemological, infrastructural and promissory: the development and introduction of digital infrastructures across each domain is tied to an emergent political economy of data that both supports and is shaped by infrastructures of algorithmic data use. By this we mean that precision is not simply premised on the integration of digital 3 technologies into material practices of healthcare, education and agriculture. But rather that the promise of precision is based on the anticipation that new forms of knowledge – and commercial opportunity – will emerge from the compilation of diverse forms of data. We suggest that these processes in turn reinforce the role of contemporary commercialization models in ways that structure the research and development on and implementation of precision technologies. We conclude the paper with observations about what emerges from these three domains, namely that: in all three there are attempts to extract new value from existing infrastructures; that precision produces evidence of its own efficacy; and, that precision is part of the invention of accuracy. Hacking (2016) characterizes the technologization and bureaucratization of numerical statistics in the nineteenth century as the ‘avalanche of printed numbers’. He contends this “statistical enthusiasm” was animated by the impulses of a conservative philanthropy that aimed at “the preservation of the established state” (p. 281). In the sections that follow we extend this line of analysis by suggesting that the promissory horizon of precision anticipates both the extrapolation of contemporary modes of bureaucratic governance and the novel renewal of relations between the state and its subjects. The movements we document in this article, such as data cooperatives and farm hack collectives, underscore the political nature of the assumptions about what is measured and how it is measured in precision agriculture, education and medicine. As we will argue below these counter currents are suggestive of the ways in which the ethical and normative contestation of big data and precision systems – often conceptualized in terms of ownership, privacy and control – is central to the realization of value in precision systems (Bronson 2020). 4 Precision Medicine and the Political Economy of Health Data A recent New York Times article by Natasha Singer entitled ‘How Big Tech Is Going After Your Health Care’ begins with the story of a young medical student, Daniel Poston, who upon opening the app store on his iPhone, sees an app for a new heart study prominently featured. Singer’s article explains the significance of this development for the contemporary medical landscape. She writes: “People often learn about new research studies through in- person conversations with their doctors. But not only did this study, run by Stanford University, use a smartphone to recruit consumers, it was financed by Apple. And it involved using an app on the Apple Watch to try to identify irregular heart rhythms.” Intrigued, the medical student, who already owned an Apple Watch, registered for the heart study. Poston muses about whether “the entire practice of medicine will be revolutionized by technology” by the time he finishes his medical degree. Indeed, Singer’s article describes how Apple, Google, Microsoft, Intel, Amazon and Facebook and other large tech companies have started making significant investments – some $2.7bn – in healthcare equity deals. Many of these investments aim to disentangle medical interventions from clinics through the development of continuous monitoring devices such as the Apple Watch or devices that detect movement in the owner’s bed (Apple has also acquired Beddit, a sleep tracking company). These examples represent a new form of the ‘datafication of health’. The novelty of this turn in the governance of health refers to both a sensibility and an accompanying set of practices related to digital devices, especially through smart phones and cloud computing. Proponents claim these developments will revolutionise therapy development, diagnosis and care—a sensibility
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