This paper is concerned with everyday data practices, considering how people record data produced through self-monitoring. The analysis unpacks the relationships between taking a measure, and making and reviewing records. The paper is based on an interview study with people who monitor their blood pressure and/or body mass index/weight. Animated by discussions of ‘data power’ which are, in part, predicated on the flow and aggregation of data, we aim to extend important work concerning the everyday constitution of digital data. In (...) the paper, we adopt and develop the idea of curation as a theory of attention. We introduce the idea of discerning work to characterise the skilful judgements people make about which readings they record, how readings are presented, and about the records they retain and those they discard. We suggest self-monitoring produces partial data, both in the sense that it embodies these judgements, and also because monitoring might be conducted intermittently. We also extend previous analyses by exploring the broad set of materials, digital and analogue, networked and not networked, involved in record keeping to consider the different ways these contributed to regulating attention to self-monitoring. By paying attention to which data is recorded and the occasions when data is not recorded, as well as the ways data is recorded, the research provides specificity to the different ways in which self-monitoring data may or may not flow or contribute to big data sets. We argue that ultimately our analysis contributes to nuancing our understanding of ‘data power’. (shrink)

Departing from popular imaginations around artificial intelligence (AI), this article engages in the I in the AI acronym but from perspectives outside of mathematics, computer science and machine learning. When intelligence is attended to here, it most often refers to narrow calculating tasks. This connotation to calculation provides AI an image of scientificity and objectivity, particularly attractive in societies with a pervasive desire for numbers. However, as is increasingly apparent today, when employed in more general areas of our messy socio-cultural (...) realities, AI- powered automated systems often fail or have unintended consequences. This article will contribute to this critique of AI by attending to Nicholas of Cusa and his treatment of intelligence. According to him, intelligence is equally dependent on an ability to handle the unknown as it unfolds in the present moment. This suggests that intelligence is organic which ties Cusa to more contemporary discussions in tech philosophy, neurology, evolutionary biology, and cognitive sciences in which it is argued that intelligence is dependent on having—and acting through—an organic body. Understanding intelligence as organic thus suggests an oxymoronic relationship to artificial. (shrink)

During the course of the COVID-19 pandemic, a wide range of digital technologies and data analytics have been incorporated into pandemic response models globally, in the hope of better detecting, tracking, monitoring and containing outbreaks. This increased digital involvement in disease control has offered the prospect of heightened effectiveness in all of the above, but not without raising other concerns. This paper contributes to ongoing discussions of the digital transformation in disease control by proposing a materialist analysis of how such (...) control has become operative and what its effects may be, both now and in the future. Using Taiwan's digital pandemic response as a case study, the paper explores specific ways in which material processes and arrangements have shaped digital measures, as well as the actions that rendered such measures operable, with their ensuing consequences. This analysis illustrates the importance of historical, material and technological specificities and contingencies to our understanding of how digital disease control takes a particular shape. It also demonstrates how shifting regimes of practice continually reconfigure the ways in which digital disease control functions. The paper argues that paying greater attention to the materialities of digital disease control can provide a more nuanced understanding of the complex ways in which society may be protected or harmed by its use, possibly simultaneously. It is hoped that such increased attentiveness may inform more considered and careful preparation for subsequent pandemics. (shrink)

Archival data processing consists of cleaning and formatting data between the moment a dataset is deposited and its publication on the archive’s website. In this article, I approach data processing by combining scholarship on invisible labor in knowledge infrastructures with a Marxian framework and show the relevance of considering data processing as factory labor. Using this perspective to analyze ethnographic data collected during a six-month participatory observation at a U.S. data archive, I generate a taxonomy of the forms of alienation (...) that data processing generates, but also the types of resistance that processors develop, across four categories: routine, speed, skill, and meaning. This synthetic approach demonstrates, first, that data processing reproduces typical forms of factory worker’s alienation: processors are asked to work along a strict standardized pipeline, at a fast pace, without acquiring substantive skills or having a meaningful involvement in their work. It reveals, second, how data processors resist the alienating nature of this workflow by developing multiple tactics along the same four categories. Seen through this dual lens, data processors are therefore not only invisible workers, but also factory workers who follow and subvert a workflow organized as an assembly line. I conclude by proposing a four-step framework to better value the social contribution of data workers beyond the archive. (shrink)

This article focuses on emotions, conceptualised as emotional labour, evoked during data practices used to repurpose and enable healthcare data journeys for Finnish public healthcare. Combined approaches from critical data studies and the sociology of emotions were used to contribute to a better understanding of the mundane but often invisible work of the emotions of experts involved in data practices, such as facilitating data journeys and building data technologies. The article is based on a two-and-a-half-year ethnographic study conducted in a (...) Finnish regional public healthcare and social service organisation. The study results were derived from the analysis of 39 interviews and fieldnotes produced by observing 170 h of various meetings, events and work activities performed by experts. The results were organised into three forms of observed experts’ emotional labour related to three phases of healthcare data journeys: caring for data production and preparing data for travel, managing excitement and frustration in data processing for continually building the data management system, and reassuring users in making sense of obtained data analytics. The results contribute to a greater understanding of the emotions and emotional labour generated by healthcare data journeys and in relation to the volatile nature of healthcare data and the collaborative character of data practices. This work advocates for a better recognition of the emotional aspects of data practices and their implications on data-based knowledge and datafication processes in healthcare. (shrink)

Algorithms are increasingly affecting us in our daily lives. They seem to be everywhere, yet they are seldom seen by the humans dealing with the consequences that result from them. Yet, in recent theorisations, there is a risk that the algorithm is being given too much prominence. This article addresses the interaction between algorithmic outputs and the humans engaging with them by drawing on studies of two distinct empirical fields – self-quantification and audit controls of taxpayers. We explore recalibration as (...) a way to understand the practices and processes involved when, on the one hand, decisions are made based on results from algorithmic calculations in counting and accounting software, and on the other hand, when decisions are made based on human experience/knowledge. In particular, we are concerned with moments when an algorithmic output differs from expectations of ‘normalcy’ and ‘normativity’ in any given situation. This could be a ‘normal’ relation between sales and VAT deductions for a business, or a ‘normal’ number of steps one takes in a day, or ‘normative’ as it is according to the book, following guidelines and recommendations from other sources. In these moments, we argue that a process of recalibration occurs – an effortful moment where, rather than treat the algorithmic output as given, individuals’ tacit knowledge, experiences and intuition are brought into play to address the deviation from the normal and normative. (shrink)

New data-driven ideas of healthcare have increased pressures to reform existing data infrastructures. This article explores the role of data governing institutions during a reform of both secondary health data infrastructure and related legislation in Finland. The analysis elaborates on recent conceptual work on data journeys and data frictions, connecting them to institutional and regulatory issues. The study employs an interpretative approach, using interview and document data. The results show the stark contrast between the goals of open and Big Data (...) inspired reforms and the existing institutional realities. The multiple tensions that emerged during the process indicate how data frictions emanate to the institutional level, and how mundane data practices and institutional dynamics are intertwined. The article argues that in the Finnish case, public institutions acted as sage-guards of public interest, preventing more controversial parts from passing. Finally, it argues that initiating regulatory and infrastructural reforms simultaneously was beneficial for solving the tensions of the initiative and analysing either side separately would have produced misleading accounts of the overall initiative. The results highlight the benefits of analysing institutional dynamics and data practices as connected issues. (shrink)