While the academic separation of policing and regulation is still largely operative, points of convergence are more significant than ever in the digital age, starting with concomitant debates about algorithms as a new figure of power. From the policing of illegal activities to the regulation of legal ones, the algorithmization of such critical social ordering practices has been the subject of growing attention. These burgeoning discussions are focused on one common element: big data surveillance. In accordance with such similarities and (...) paralleled developments in policing and regulation, the article aims to further bridge the gap between literatures to respond to the calls for studying big data surveillance across institutional domains and social fields. To do so, it is focused on one case study that articulates algorithmic policing and regulation domains, in-between security and economic fields. This is the fight against illicit finance, i.e. ‘the global action against the financial flows that fuel crime and terrorism’. To what extent does big data surveillance make a difference in the main global policy of crime-fighting and financial regulation? Drawing on a fieldwork in a large North American bank, the present article takes stock of the algorithmic overlap between policing and regulation. It argues that the final result is policing and regulation of neither too much nor too little, which gives rise to automated and everyday mass surveillance while remaining as far removed from regulatory and crime-fighting ambitions as it is from dystopian visions of big data and algorithmic drama. (shrink)

Policy discussions and corporate strategies on machine learning are increasingly championing data reuse as a key element in digital transformations. These aspirations are often coupled with a focus on responsibility, ethics and transparency, as well as emergent forms of regulation that seek to set demands for corporate conduct and the protection of civic rights. And the Protective measures include methods of traceability and assessments of ‘good’ and ‘bad’ datasets and algorithms that are considered to be traceable, stable and contained. However, (...) these ways of thinking about both technology and ethics obscure a fundamental issue, namely that machine learning systems entangle data, algorithms and more-than-human environments in ways that challenge a well-defined separation. This article investigates the fundamental fallacy of most data reuse strategies as well as their regulation and mitigation strategies that data can somehow be followed, contained and controlled in machine learning processes. Instead, the article argues that we need to understand the reuse of data as an inherently entangled phenomenon. To examine this tension between the discursive regimes and the realities of data reuse, we advance the notion of reuse entanglements as an analytical lens. The main contribution of the article is the conceptualization of reuse that places entanglements at its core and the articulation of its relevance using empirical illustrations. This is important, we argue, for our understanding of the nature of data and algorithms, for the practical uses of data and algorithms and our attitudes regarding ethics, responsibility and regulation. (shrink)

Smart cities build on the idea of collecting data about the city in order for city administration to be operated more efficiently. Within a research project gathering an interdisciplinary team of researchers – engineers, designers, gender scholars and human geographers – we have been working together using participatory design approaches to explore how paying attention to the diversity of human needs may contribute to making urban spaces comfortable and safe for more people. The project team has deployed sensors collecting data (...) on air quality, sound and mobility in a smart city testbed in Norrköping, Sweden. While these sensors are meant to capture an accurate ‘map’ of the street and what is going on along it, our interdisciplinary conversations around the sensors have revealed the heterogeneity both of smart city planning and spatial formulations of the city. The discussions have given rise to questions regarding the work that goes into constructing the sensor box itself, as well as the work of deploying it, and how these influence the ‘map’ that the sensors produce. In this paper, we draw on Lefebvre to explore how the sensors themselves produce smart spaces. We analyze how the box depends on perceived space to function (e.g. requiring electricity), and simultaneously it produces conceptualizations of space that are influenced by the materiality of the box itself (e.g. sensors being affected by heat and noise). Further, we explore how the (in)visibility of sensor technology influences lived space. (shrink)

Efforts to address algorithmic harms have gathered particular steam over the last few years. One area of proposed opportunity is the notion of an “algorithmic audit,” specifically an “internal audit,” a process in which a system’s developers evaluate its construction and likely consequences. These processes are broadly endorsed in theory—but how do they work in practice? In this paper, we conduct not only an audit but an autoethnography of our experiences doing so. Exploring the history and legacy of a facial (...) recognition dataset, we find paradigmatic examples of algorithmic injustices. But we also find that the process of discovery is interwoven with questions of affect and infrastructural brittleness that internal audit processes fail to articulate. For auditing to not only address existing harms but avoid producing new ones in turn, we argue that these processes must attend to the “mess” of engaging with algorithmic systems in practice. Doing so not only reduces the risks of audit processes but—through a more nuanced consideration of the emotive parts of that mess—may enhance the benefits of a form of governance premised entirely on altering future practices. (shrink)

This article examines how algorithmic accountability is translated into action at the municipal level in the United States. Based on a review of task forces, ordinances, and policy toolkits from New York City and Seattle, I demonstrate the ways municipalities and local publics operationalize abstract notions of accountability. Municipal interventions often prioritize revealing computational tools (transparency) and their effects on people (impact assessments). While these two forms of accountability are crucial, they may neglect to examine institutions—and how they change—as they (...) incorporate automated decision systems. I thus propose a political-economic approach that recognizes algorithmic systems as part of municipal institutions and focuses on their role in intensifying data collection and commodification between public agencies and markets. I argue that algorithmic accountability, especially in public agencies, needs to focus on incompetence and asymmetries of power within a network of governments, tech companies, community groups, and technologies. With a mix of transparency, impact assessments, and political economic review, the paper proposes a more comprehensive assessment of automated decision systems through their development, procurement, use, impact, and decommissioning. (shrink)

This commentary article outlines and explores the key problem that faces anyone interested in researching and understanding what might be thought of as a recursive society. It reflects on the problem that is posed by the layering of multiple feedback loops as a result of algorithmic sorting and data processes. This article is concerned with the difficulties of understanding the social where recursive algorithmic processes have repeatedly shaped outcomes, practices, relations and actions over time. This is not just about the (...) sinking of algorithms into the everyday, it is about the way that loop-upon-loop of data processes lead to the social world itself being recursive. This repeated looping is described here as a kind of data coiling. The article argues for a focus on recursivity and for an engagement with the conceptual problems and questions that this notion implies. (shrink)

Personal data are produced through our daily interactions with digital technologies like search engines, social media, and online shopping, and is often referred to as our “digital exhaust.” It has been characterized as the key resource or asset for our economies in the 21st century. This paper focuses on the socio-technical imaginaries of digital personal data as a way to understand how desired forms of data governance are co-produced with collective understandings of personal data as a political-economic asset. We examine (...) the different socio-technical imaginaries that underpinned different developments in data regulations in the United States and EU from 2008 to 2016, focusing specifically on the mutual constitution of law, political economy, and technoscience. We do so in order to understand the “prehistories” of contemporary data governance. We analyze the institutional and legal context around the development of data privacy regulation and data commercialization in these two important jurisdictions and reflect on how this institutional and legal context configured their respective approaches to data governance. (shrink)

This article proposes an ethical framework to navigate life in the datafied world that combines the relational ethics approach of the philosopher Paul Ricoeur with the idiom of co-production from the field of Science and Technology Studies (STS). Mainstream data and computing ethics approaches, which tend to view ethics itself as a technology to produce particular outcomes, fail to adequately consider the context of the datafied world for ethics. The datafied world is a condition in which data and computing technologies (...) form the ineluctable infrastructure for daily life, structuring social order, forming power relations, and supporting visions of desirable futures. I argue that the datafied world is not just a background upon which ethics unfolds, rather it demands a novel framework for ethics that understands the contexts of data and computing technologies and their consequences for human action. The idiom of co-production suggests how action gets tied to visions of the good in the datafied world. In particular, it draws attention to the evolution of these actions in the identities, institutions, representations, and discourses of the social world, creating specific forms of life and meaning in datafied societies. (shrink)

Drawing on a qualitative, multi-case study of three kinds of geographically tethered gig work—ride-hailing, delivery, and domestic services platforms—in the United States, I examine how workers anticipate the influences of metrics, live with metrics, and cope with algorithmic precarity. Data for this project include in-depth interviews with 50 gig workers about their efforts to interpret and manage metrics as part of their everyday work practices. The analysis reveals that participants were anxious about metrics primarily because of the disciplinary outcomes, that (...) are, the threat of job loss and the valued job features. It also directs attention to how workers felt and experienced customer-sourced ratings and system-generated behavioral metrics variously across platforms. Information asymmetries and the perceived lack of control also intensified a sense of powerlessness among participants. While participants articulated strategies that aimed at managing the uncertainty of customer-sourced ratings—and more precisely, the work-related uncertainty created by “difficult customers”—throughout service interactions, their feelings of anxiety could not be resolved. Furthermore, the (in)visibility of metrics, the settings of platform-mediated worker–customer interactions, and workers’ platform dependence contributed to the varying disciplinary power of metrics. The study contributes to understanding how metrics as affective measures mediate the trilateral relationship between platforms, workers, and customers in the gig economy. (shrink)

As a key constituent of China's approach to fighting COVID-19, Health Code apps (HCAs) not only serve the pandemic control imperatives but also exercise the agency of digital surveillance. As such, HCAs pave a new avenue for ongoing discussions on contact tracing solutions and privacy amid the global pandemic. This article attends to the perceived privacy protection among HCA users via the lens of the contextual integrity theory. Drawing on an online survey of adult HCA users in Wuhan and Hangzhou (...) (N = 1551), we find users’ perceived convenience, attention towards privacy policy, trust in government, and acceptance of government purposes regarding HCA data management are significant contributors to users’ perceived privacy protection in using the apps. By contrast, users’ frequency of mobile privacy protection behaviors has limited influence, and their degrees of perceived protection do not vary by sociodemographic status. These findings shed new light on China's distinctive approach to pandemic control with respect to the state's expansion of big data-driven surveillance capacity. Also, the findings foreground the heuristic value of contextual integrity theory to examine controversial digital surveillance in non-Western contexts. Put tougher, our findings contribute to the thriving scholarly conversations around digital privacy and surveillance in China, as well as contact tracing solutions and privacy amid the global pandemic. (shrink)

This study examines how visibility of a content moderator and ambiguity of moderated content influence perception of the moderation system in a social media environment. In the course of a two-day pre-registered experiment conducted in a realistic social media simulation, participants encountered moderated comments that were either unequivocally harsh or ambiguously worded, and the source of moderation was either unidentified, or attributed to other users or an automated system (AI). The results show that when comments were moderated by an AI (...) versus other users, users perceived less accountability in the moderation system and had less trust in the moderation decision, especially for ambiguously worded harassments, as opposed to clear harassment cases. However, no differences emerged in the perceived moderation fairness, objectivity, and participants confidence in their understanding of the moderation process. Overall, our study demonstrates that users tend to question the moderation decision and system more when an AI moderator is visible, which highlights the complexity of effectively managing the visibility of automatic content moderation in the social media environment. (shrink)

This article examines algorithmic content moderation, using the moderation of violent extremist content as a specific case. In recent years, algorithms have increasingly been mobilized to perform essential moderation functions for online social media platforms such as Facebook, YouTube, and Twitter, including limiting the proliferation of extremist speech. Drawing on Katherine Hayles’ concept of “cognitive assemblages” and the Critical Security Studies literature, we show how algorithmic regulation operates within larger assemblages of humans and non-humans to influence the surveillance and regulation (...) of information flows. We argue that the dynamics of algorithmic regulation are more liquid, cobbled together and distributed than it appears. It is characterized by a set of shifting human and machine entities, which mix traditional surveillance methods with more sophisticated tools, and whose linkages and interactions are transient. The processes that enable the consolidation of knowledge about risky profiles and contents are, therefore, collective and distributed among humans and machines. This allows us to argue that the cognitive assemblages involved in content moderation become a cobbled space of preemptive calculation. (shrink)

This article theorizes the relationship between two ways of “seeing” and organizing urban mobility markets: the abstract, algorithmic vision of the mobility platform and the experiential, relational vision of the platform driver. Using the case of mobility platforms in Jakarta, we empirically demonstrate how drivers experience the limitations of the platform's visions and how they deploy their own alternative visions of work and the city. We offer this drivers’ “View from Within” as a counterpoint to the visions of the platform, (...) decentering the platform's visions as the sole arbiter of change and optimization in the city. At the same time, we disrupt the assumed binary between these views, showing how they exist in a complex dance of complementarity and contestation. We conclude with a discussion on the opportunities this entanglement presents for worker agency in the algorithmic market, the hurdles toward more “worker centered design” in platform economies and the tensions between globalizing technological solutions and their localized instantiations. Through this article, we argue for seeing deep, embedded relationships as culturally and historically important modes of urban life which technology has to interact with but cannot fully capture nor do away with. (shrink)

Autonomous vehicle moral dilemmas matter less for the particular outcomes of potential accidents than for their role in defining the values of the society we wish to live in. Different approaches have been suggested to determine the ethical settings that autonomous vehicles should be implemented in and identify the legitimate agents for making such decisions. Most of these, however, fail on theoretical grounds, facing severe issues related to moral justifications and compliance to the law, or on practical grounds, being insufficiently (...) universal, action-guiding, or technically viable to be implemented. The analogy with the “trolley problem” has been extensively discussed. However, researchers have rarely tried to adapt this framework to autonomous vehicle cases or investigate how it could be used to address these issues. In doing so, this paper aims to answer the two key problems of autonomous vehicle dilemmas. With regards to the decision-maker, it rejects autonomous vehicle users’ choice-based models, showing the absurdity of both switch of control and adaptative preferences and arguing for common legislator-determined ethical settings. With regards to the decisions themselves, it criticizes both utilitarian views and those based on individuals’ criteria to suggest a deontologist rights-based approach. This allows for the defence of a morally coherent, regulatory compliant, explainable, and easily implementable framework capable of addressing all autonomous vehicle moral dilemma scenarios present in the literature. (shrink)

Machine learning systems are making considerable inroads in society owing to their ability to recognize and predict patterns. However, the decision-making logic of some widely used machine learning models, such as deep neural networks, is characterized by opacity, thereby rendering them exceedingly difficult for humans to understand and explain and, as a result, potentially risky to use. Considering the importance of addressing this opacity, this paper calls for research that studies empirically and theoretically how machine learning experts and users seek (...) to attain machine learning explainability. Focusing on automated trading, we take steps in this direction by analyzing a trading firm’s quest for explaining its deep neural network system’s actionable predictions. We demonstrate that this explainability effort involves a particular form of human–machine interaction that contains both anthropomorphic and technomorphic elements. We discuss this attempt to attain machine learning explainability in light of reflections on cross-species companionship and consider it an example of human–machine companionship. (shrink)

In this paper, we draw on Foucault's and Deleuze's theorisations of discipline and control, respectively, to understand a teacher accountability system in the US state of Texas: the Texas Teacher Evaluation and Support System (hereafter, T-TESS). Specifically, we focus on the interplay of physical and virtual modes of governance – which we develop here as physical–virtual hybridity – and the techniques that make these physical and virtual domains compatible via language interoperability, with T-TESS deployed as a representative empirical case to (...) show how such technologies work to govern teacher subjectivity. First, in-person appraiser meetings and observations re-code teachers’ linguistic behaviours, so their physical bodies and practices can become legible to and interoperable with the hybrid T-TESS system. This avoids any possible syntax errors between the linguistic expression of physical teacher speech and the digital coding language of T-TESS. Second, these digital bodies of data can now be viewed as proxies for the physical teacher body in the classroom, allowing the constant modulation in physical space (teacher bodies) and digital space (bodies of data). This is the physical–virtual hybridity of T-TESS, whereby discipline and control work symbiotically to govern both the physical and the digital expressions of teachers and their teaching. In this way, the disciplining of teachers’ language has profound effects on teachers’ bodies, both corporeal and digital. (shrink)

With the surge in the number of data and datafied governance initiatives, arrangements, and practices across the globe, understanding various types of such initiatives, arrangements, and their structural causes has become a daunting task for scholars, policy makers, and the public. This complexity additionally generates substantial difficulties in considering different data(fied) governances commensurable with each other. To advance the discussion, this study argues that existing scholarship is inclined to embrace an organization-centric perspective that primarily concerns factors and dynamics regarding data (...) and datafication at the organizational level at the expense of macro-level social, political, and cultural factors of both data and governance. To explicate the macro, societal dimension of data governance, this study then suggests the term “social data governance” to bring forth the consideration that data governance not only reflects the society from which it emerges but also (re)produces the policies and practices of the society in question. Drawing on theories of political science and public management, a model of social data governance is proposed to elucidate the ideological and conceptual groundings of various modes of governance from a comparative perspective. This preliminary model, consisting of a two-dimensional continuum, state intervention and societal autonomy for the one, and national cultures for the other, accounts for variations in social data governance across societies as a complementary way of conceptualizing and categorizing data governance beyond the European standpoint. Finally, we conduct an extreme case study of governing digital contact-tracing techniques during the pandemic to exemplify the explanatory power of the proposed model of social data governance. (shrink)

Algorithmic decision-making increasingly shapes people's daily lives. Given that such autonomous systems can cause severe harm to individuals and social groups, fairness concerns have arisen. A human-centric approach demanded by scholars and policymakers requires considering people's fairness perceptions when designing and implementing algorithmic decision-making. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 58 empirical studies spanning multiple domains and scientific disciplines. Through thorough coding, we systemize the current empirical literature along (...) four dimensions: (1) algorithmic predictors, (2) human predictors, (3) comparative effects (human decision-making vs. algorithmic decision-making), and (4) consequences of algorithmic decision-making. While we identify much heterogeneity around the theoretical concepts and empirical measurements of algorithmic fairness, the insights come almost exclusively from Western-democratic contexts. By advocating for more interdisciplinary research adopting a society-in-the-loop framework, we hope our work will contribute to fairer and more responsible algorithmic decision-making. (shrink)

Emotion recognition, and in particular acial emotion recognition (FER), is among the most controversial applications of machine learning, not least because of its ethical implications for human subjects. In this article, we address the controversial conjecture that machines can read emotions from our facial expressions by asking whether this task can be performed reliably. This means, rather than considering the potential harms or scientific soundness of facial emotion recognition systems, focusing on the reliability of the ground truths used to develop (...) emotion recognition systems, assessing how well different human observers agree on the emotions they detect in subjects’ faces. Additionally, we discuss the extent to which sharing context can help observers agree on the emotions they perceive on subjects’ faces. Briefly, we demonstrate that when large and heterogeneous samples of observers are involved, the task of emotion detection from static images crumbles into inconsistency. We thus reveal that any endeavour to understand human behaviour from large sets of labelled patterns is over-ambitious, even if it were technically feasible. We conclude that we cannot speak of actual accuracy for facial emotion recognition systems for any practical purposes. (shrink)

With technologies like machine learning and data analytics being deployed as privileged means to improve how contemporary bureaucracies work, many governments around the world have turned to artificial intelligence as a tool of statecraft. In that context, our paper uses Canada as a critical case to investigate the relationship between ideals of good government and good technology. We do so through not one, but two Trudeaus—celebrity Prime Minister Justin Trudeau (2015—…) and his equally famous father, former Prime Minister Pierre Elliott (...) Trudeau (1968–1979, 1980–1984). Both shared a similar interest in new ideas and practices of both intelligent government and artificial intelligence. Influenced by Marshall McLuhan and his media theory, Pierre Elliott Trudeau deployed new communication technologies to restore centralized control in an otherwise decentralized state. Partly successful, he left his son with an informationally inclined political legacy, which decades later animated Justin Trudeau's own turn toward Big Data and artificial intelligence. Compared with one another, these two visions for both government and artificial intelligence illustrate the broader tensions between cybernetic and neoliberal approaches to government, which inform how new technologies are conceived of, and adopted, as political ones. As this article argues, Canada offers a paradigmatic case for how artificial intelligence is as much shaped by theories of government as by investments and innovations in computing research, which together delimit the contours of intelligence by defining which technical systems, people, and organizations come to be recognized as its privileged bearers. (shrink)

Digital Earth applications provide a common ground for visualizing, simulating, and modeling real-world situations. The potential of Digital Earth applications has increased significantly with the evolution of artificial intelligence systems and the capacity to collect and process complex amounts of geospatial data. Yet, the widespread techno-optimism at the root of Digital Earth must now confront concerns over high-risk artificial intelligence systems and power asymmetries of a datafied society. In this commentary, we claim that not only can current debates about data (...) governance and ethical artificial intelligence inform development in the field of Digital Earth, but that the specificities of geospatial data, together with the expectations surrounding Digital Earth applications, offer a fruitful lens through which to examine current debates on data governance and artificial intelligence ethics. In particular, we argue that for the implementation of ethical artificial intelligence and inclusive approaches to data governance, Digital Earth initiatives need to involve stakeholders and communities at the local level and be sensitive to social, legal, cultural, and institutional contexts, including conflicts that might arise within those contexts. (shrink)

Firms are increasingly personalising their offers and services, leading to an ever finer-grained segmentation of consumers online. Targeted online advertising and online price discrimination are salient examples of this development. While personalisation's overall effects on consumer welfare are expectably ambiguous, it can lead to concentration in the distribution of advertising and commercial offers. Constellations are possible in which a market is generally open to competition, but the targeted consumer is only made aware of one possible seller. For the consumer, such (...) a market could effectively resemble a monopoly. We call such extreme cases ‘targeting pockets’. Competition-law metrics such as the Herfindahl–Hirschman Index and traditional means of public oversight of adverts would not detect this concentration. We, therefore, suggest a novel metric, the Concentration-after-Personalisation Index (CAPI). The CAPI treats every consumer as a separate ‘market’, computes a measure of concentration for personalised adverts and offers for each individual consumer separately, and then averages the result to measure the exposure experienced by an average consumer. We demonstrate how the CAPI can serve as a monitoring tool for regulators and auditors and thus help to enforce existing consumer law as well as proposed new regulations such as the European Union's Digital Services Act and its Artificial Intelligence Act. We further show how adding noise via randomly distributed non-personalised adverts can dilute the potential harm of overly concentrated personalisation. We demonstrate how the CAPI can identify the optimal degree of added noise, balancing the protection of consumer choice with the economic interests of advertisers. (shrink)

There is little consensus on what artificial intelligence (AI) systems may or may not embrace. Although this may point to multiplicity of interpretations and backgrounds, a lack of conceptual clarity could thwart the development of common ground around the concept among researchers, practitioners and users of AI and pave the way for misinterpretation and abuse of the concept. This article argues that one of the effective ways to delineate the concept of AI is to compare and contrast it with human (...) intelligence. In doing so, the article broaches the unique capabilities of humans and AI in relation to one another (human and machine tacit knowledge), as well as two types of AI systems: one that goes beyond human intelligence and one that is necessarily and inherently tied to it. It finally highlights how humans and AI can augment their capabilities and intelligence through synergistic human–AI interactions (i.e., human-augmented AI and augmented human intelligence), resulting in hybrid intelligence, and concludes with a future-looking research agenda. (shrink)

Decision support systems, which incorporate artificial intelligence and big data, are receiving significant attention in the public sector. Decision support systems are sociocultural artefacts that are subject to a mix of technical and political choices, and critical investigation of these choices and the rationales they reflect are paramount since they are inscribed into and may cause harm, violate fundamental rights and reproduce negative social patterns. Applying and merging the concepts of sense-making and translation, this article investigates the rationales, translations and (...) critical reflections that shape the development of a decision support system to support social workers assessing referrals concerning child neglect. It presents findings from a qualitative case study conducted in 2019–2020 at the Citizen Centre Children and Young People, Copenhagen Municipality, Denmark. The analysis shows how key actors through processes of translation construct, negotiate and readjust problem definitions, roles, interests, responsibilities and ideas of ambiguity and accountability. Although technological solutionism is present in these processes, it is not the only rationale invested. Rather, technological and data-driven rationales are adjusted to and merged with rationales of efficiency, return on investment and child welfare. Through continuous renegotiation of roles, responsibilities and problems according to these rationales, the key actors attempt to orchestrate ways of managing the complexity facing child welfare services by projecting images of future potentials of the decision support system that are yet to be realised. (shrink)

This commentary tests a methodology proposed by Munk et al. (2022) for using failed predictions in machine learning as a method to identify ambiguous and rich cases for qualitative analysis. Using a dataset describing actions performed by fictional characters interacting with machine vision technologies in 500 artworks, movies, novels and videogames, I trained a simple machine learning algorithm (using the kNN algorithm in R) to predict whether or not an action was active or passive using only information about the fictional (...) characters. Predictable actions were generally unemotional and unambiguous activities where machine vision technologies were treated as simple tools. Unpredictable actions, that is, actions that the algorithm could not correctly predict, were more ambivalent and emotionally loaded, with more complex power relationships between characters and technologies. The results thus support Munk et al.'s theory that failed predictions can be productively used to identify rich cases for qualitative analysis. This test goes beyond simply replicating Munk et al.'s results by demonstrating that the method can be applied to a broader humanities domain, and that it does not require complex neural networks but can also work with a simpler machine learning algorithm. Further research is needed to develop an understanding of what kinds of data the method is useful for and which kinds of machine learning are most generative. To support this, the R code required to produce the results is included so the test can be replicated. The code can also be reused or adapted to test the method on other datasets. (shrink)

This research commentary proposes a conceptual framework for studying big tech companies as “technical systems” that organize much of their operation around the mastery and operationalization of key technologies that facilitate and drive their continuous expansion. Drawing on the study of Large Technical Systems (LTS), on the work of historian Bertrand Gille, and on the economics of General Purpose Technologies (GPTs), it outlines a way to study the “tech” in “big tech” more attentively, looking for compatibilities, synergies, and dependencies between (...) the technologies created and deployed by these companies. Using Google as example, the paper shows how to interrogate software and hardware through the lens of transversal applicability, discusses software and hardware integration, and proposes the notion of “data amalgams” to contextualize and complicate the notion of data. The goal is to complement existing vectors of “big tech” critique with a perspective sensitive to the specific materialities of specific technologies and their possible consequences. (shrink)

This paper identifies and addresses persistent gaps in the consideration of ethical practice in ‘technology for good’ development contexts. Its main contribution is to model an integrative approach using multiple ethical frameworks to analyse and understand the everyday nature of ethical practice, including in professional practice among ‘technology for good’ start-ups. The paper identifies inherent paradoxes in the ‘technology for good’ sector as well as ethical gaps related to (1) the sometimes-misplaced assignment of virtuousness to an individual; (2) difficulties in (...) understanding social constraints on ethical action; and (3) the often unaccounted for mismatch between ethical intentions and outcomes in everyday practice, including in professional work associated with an ‘ethical turn’ in technology. These gaps persist even in contexts where ethics are foregrounded as matters of concern. To address the gaps, the paper suggests systemic, rather than individualized, considerations of care and capability applied to innovation settings, in combination with considerations of virtue and consequence. This paper advocates for addressing these challenges holistically in order to generate renewed capacity for change at a systemic level. (shrink)

Through a critical analysis of recent developments in the theory and practice of data science, including nascent feminist approaches to data collection and analysis, this commentary aims to signal the need for a transnational feminist orientation towards data science. I argue that while much needed in the context of persistent algorithmic oppression, a Western feminist lens limits the scope of problems, and thus—solutions, critical data scholars, and scientists can consider. A resolutely transnational feminist approach on the other hand, can provide (...) data theorists and practitioners with the hermeneutic tools necessary to identify and disrupt instances of injustice in a more inclusive and comprehensive manner. A transnational feminist orientation to data science can pay particular attention to the communities rendered most vulnerable by algorithmic oppression, such as women of color and populations in non-Western countries. I present five ways in which transnational feminism can be leveraged as an intervention into the current data science canon. (shrink)

Scholars of critical algorithmic studies, including those from geography, anthropology, Science and Technology Studies and communication studies, have begun to consider how algorithmic devices and platforms facilitate democratic practices. In this article, I draw on a comparative ethnography of two alternative open-source algorithmic platforms – Decide Madrid and vTaiwan – to consider how they are dynamically constituted by differing algorithmic–human relationships. I compare how different algorithmic–human relationships empower citizens to influence political decision-making through proposing, commenting, and voting on the urban (...) issues that should receive political resources in Taipei and Madrid. I argue that algorithmic empowerment is an emerging process in which algorithmic–human relationships orient away from limitations and towards conditions of plurality, actionality, and power decentralisation. This argument frames algorithmic empowerment as bringing about empowering conditions that allow (underrepresented) individuals to shape policy-making and consider plural perspectives for political change and action, not as an outcome-driven, binary assessment (i.e. yes/no). This article contributes a novel, situated, and comparative conceptualisation of algorithmic empowerment that moves beyond technological determinism and universalism. (shrink)

In 2020, the European Union announced the award of the contract for the biometric part of the new database for border control, the Entry Exit System, to two companies: IDEMIA and Sopra Steria. Both companies had been previously involved in the development of databases for border and migration management. While there has been a growing amount of publicly available documents that show what kind of technologies are being implemented, for how much money, and by whom, there has been limited engagement (...) with digital methods in this field. Moreover, critical border and security scholarship has largely focused on qualitative and ethnographic methods. Building on a data feminist approach, we propose a transdisciplinary methodology that goes beyond binaries of qualitative/quantitative and opacity/transparency, examines power asymmetries and makes the labour of coding visible. Empirically, we build and analyse a dataset of the contracts awarded by two European Union agencies key to its border management policies – the European Agency for Large-Scale Information Systems (eu-LISA) and the European Border and Coast Guard Agency (Frontex). We supplement the digital analysis and visualisation of networks of companies with close reading of tender documents. In so doing, we show how a transdisciplinary methodology can be a device for making datafication ‘intelligible’ at the European Union borders. (shrink)

Current challenges to sustaining public support for health data research have directed attention to the governance of data-intensive health research networks. Accountability is hailed as an important element of trustworthy governance frameworks for data-intensive health research networks. Yet the extent to which adequate accountability regimes in data-intensive health research networks are currently realized is questionable. Current governance of data-intensive health research networks is dominated by the limitations of a drawing board approach. As a way forward, we propose a stronger focus (...) on accountability as learning to achieve accountable governance. As an important step in that direction, we provide two pathways: (1) developing an integrated structure for decision-making and (2) establishing a dialogue in ongoing deliberative processes. Suitable places for learning accountability to thrive are dedicated governing bodies as well as specialized committees, panels or boards which bear and guide the development of governance in data-intensive health research networks. A continuous accountability process which comprises learning and interaction accommodates the diversity of expectations, responsibilities and tasks in data-intensive health research networks to achieve responsible and effective governance. (shrink)

Attempts to use Big Data to transform car insurance pricing in France have failed. Why? Three possible explanations are discussed: organisational and cognitive inertia, normative preventions, and deliberate strategy. This article finds that moral or political reluctance has played only a secondary role in the failure of telematics devices. More important has been the deployment of an experimental strategy that has resulted in the conclusion that in the short and medium term at least, the use of big data to rate (...) car insurance is not profitable. There are too many organisational and cognitive barriers to the smooth adoption of innovation. All insurers note they all arrived at this conclusion. An implicit consensus therefore remains to retain the old business model. (shrink)

The financial applications of data technology have enabled the rise of Chinese fintech industries. As part of people's everyday lives, fintech apps have helped companies collect vast amounts of user data for business profit and social good. This paper takes an open-systems approach to study the constructs of this emerging idea of data governance, particularly its operational logic, involved stakeholders, and socio-cultural consequences in the context of fintech industries in China. It asserts that data governance at the company level has (...) been realized through three types of tasks: standardization, configuration, and monetization of big data. These tasks are oriented by the imperative of business innovation which is considered pivotal for the company to survive the competition. An ensemble of internal and external actors joined the governing process, but internal actors hold the stake. The goal of innovation drives the companies to speedily collect data from a wider variety of sources, thus having to establish more sophisticated systems to manage and utilize those data. These complicated systems, however, have urged the government to strengthen regulatory controls alongside the general support of business innovation. This study unveils the performative aspects of big-data-based innovation at fintech firms. It also helps to understand the pathology of the innovation-governance paradox shared in the data economy in the global context. (shrink)

This special theme brings together reflections and deliberations regarding the design, implementation, and development of data governance. By addressing “social data governance” as the keyword of the special theme, we aim to further the discussion on a contextual understanding of both the governing foundations and effects of data, dataism, and datafication in different societies. Such a discussion reminds us to pay particular attention to—and thus account for—the social dynamics that underpin and contextualize the design, operation, and promotion of quantified governing (...) mechanisms in which information on social behaviors is collected, datafied, manipulated, and represented. Essentially, the social dynamics of data governance have existed for a long time and in many forms, ranging from credit bureaus’ scrutiny, evaluation, and labeling of their customers to internet-enabled massive data collection and scoring systems used by governments, and to automated contact tracing techniques as a centerpiece of dataveillance and infection control amid the COVID-19 pandemic. Nevertheless, scholarly work from a wide range of disciplines like law, mathematics, and business and with diverse geographical foci has not yet been comparatively and reflectively articulated. Being rich and diverse, the special theme advances such a requisite understanding of the status and relevance of social dynamics of data governance mechanisms based on a wide range of empirical cases around the globe. To scrutinize the social dynamics helps illuminate and contrast divergent manifestations of data governance and their underlying mechanisms. (shrink)

Algorithmic technologies and artificial intelligence are centred on data and generate new business models, known as the data-driven economy. In the European Union context, the development of such new business is accompanied by a regulatory and political framework. An important aspect of this regulatory framework regards the legal conditions that enable the data collection, availability, sharing, use and reuse. Within the larger context, this article analyses the development of the European Union regulatory framework governing the availability, sharing and reuse of (...) public sector data, also referred to as Public Sector Information policy. Anchored in the analytical tools provided by Discursive Institutionalism and Critical Data Studies and after studying the evolution of this policy over 25 years, this article argues that economic considerations have been overwhelmingly decisive in the European Union Public Sector Information policy and much less attention has been paid to fundamental rights and democracy issues. It also shows how European Union Public Sector Information policy contributes to the data infrastructure, enabling a thriving data-driven economy. In doing so, this article argues that the possible problematic effects of this new data-driven economy are not only affordances of the technology itself but are also the result of political and regulatory choices. More globally, the article stresses the need for policymakers to inscribe each of the policies and regulations affecting the digital transformation in the framework of fundamental rights and democracy. (shrink)

The rise of digital platforms is growingly acknowledged as a pivotal characteristic of contemporary capitalism. A subset of the literature on digital platforms scrutinized the political economy of platform companies, their data-driven business models along with the economic, social, and political consequences of platformization. Yet, this strand has been overwhelmingly interested in Big Tech companies in the United States and China. In this article, I aim to expand the geographical span of this emerging literature by scrutinizing platformization dynamics in Turkey, (...) a middle-income country that has not been systematically examined so far along these lines. To this end, I present an assessment on the overall platform landscape in Turkey by focusing on three highlight digital platforms (Trendyol, Hepsiburada, and Getir). My analysis on the trajectory of these three platforms indicates the following. First, these platforms have exhibited meteoric growth rates and have risen among the most valuable companies in Turkey in a tremendously short period of time. Second, their aggressive growth strategies based on data extraction and network externalities have led to a strong monopolization drive. Third, they have rapidly expanded into adjacent business areas, transforming themselves into infrastructural components of the domestic economy, with profound impacts on production, trade, and labor relations. Fourth, they remain dependent on the infrastructure laid by Big Techs. These findings suggest that platformization is rapidly transforming economic landscapes other than the United States and China as well, yet in an uneven and dependent form, underlining the need to broaden the geographical focus of the concerned literature. (shrink)

This study examines how mainstream political actors and other organizations use political targeted messages. For this purpose, a data set from ProPublica is used. The study examines 55,918 sponsored Facebook ads that were posted by 236 political actors (i.e., political elites and other organizations) in the United States. (1) Topic classification was used to identify policy issues, (2) network analysis to identify the main policy issues from the various political actors, and (3) Sankey diagrams to visualize microtargeted messages. Our findings (...) indicate that actors focus on traditionally owned issues (i.e., the Democratic Party: environmental policy, social issues, and social welfare; the Republican Party: foreign affairs, law, and government finances). No clear evidence for a focus on wedge issues can be found, however, some first indications (e.g., a focus on reproductive rights, LGBTQ+) are present in a targeted media environment. All in all, the current study helps us to understand in what way political actors deploy targeted messages. (shrink)

The technological solutions adopted during the current pandemic will have a lasting impact on our societies. Currently, COVID-19 health status certificates are being deployed around the world, including in Europe, the United States and China. When combined with identity verification, these digital and paper-based certificates allow individuals to prove their health status by showing recent COVID-19 tests results, full vaccination records or evidence of recovery from COVID-19. Most countries in the Global South, where vaccination rates are low, have not yet (...) fully implemented such certificates, although several initiatives are currently underway. That is, for instance, the case in the African Union. Yet, it is not sufficient to develop technical solutions for the verification of COVID-19 health status. Because technologies do not evolve in a legal vacuum, the existing laws and regulations must be respected. The risks of implementing such technologies must be anticipated and mitigated as much as possible before any large-scale deployment. Risk mitigation should also underpin strategies throughout the deployment of these certificates. This article evaluates the key legal implications of COVID-19 health status certificates for data privacy and human rights. In doing so, it contributes to the current debates, thus informing policymakers in this area of vital national and international interest. (shrink)

Datafication of social life affects what society regards as knowledge. Jasanoff’s regimes of sight framework provides three ideal-type models of authorised knowing in environmental data practice. This paper applies Jasanoff's framework for analysing intelligence practice through an exploratory empirical study of crime and intelligence practitioners in a selection of police services in Australia, New Zealand, Canada and the United States. The paper argues that the ‘view from somewhere’ captures the essence of existing police intelligence practices in the four countries but (...) the ‘view from nowhere’ is emerging as a possible future for police intelligence – an approach promoted by technology companies and supported mainly by police leaders and managers. The paper investigates the challenges and limits of a shift by police from VFS to VFN in the production of intelligence; the challenges are primarily political, which threaten the dominance of police contextual knowledge over ‘scientific’ knowledge. These political challenges also have symbolic and material implications. The paper concludes that, because of these challenges, a complete shift from VFS to VFN is not likely to happen. At best the two models might co-exist with the latter subordinate to the imperatives of the former, resulting in further tension between sworn officers and civilians, organisational inertia, as well as technologies that may be under-utilised or abandoned. (shrink)

Social media commentary, satellite imagery and GPS data are a part of ‘alternative data’, that is, data that originate outside of the standard repertoire of market data but are considered useful for predicting stock prices, detecting different risk exposures and discovering new price movement indicators. With the availability of sophisticated machine-learning analytics tools, alternative data are gaining traction within the investment management and algorithmic trading industries. Drawing on interviews with people working in investment management and algorithmic trading firms utilizing alternative (...) data, as well as firms providing and sourcing such data, we emphasize social media-based sentiment analytics as one manifestation of how alternative data are deployed for stock price prediction purposes. This demonstrates both how sentiment analytics are developed and subsequently utilized by investment management firms. We argue that ‘alternative data’ are an open-ended placeholder for every data source potentially relevant for investment management purposes and harnessing these disparate data sources requires certain standardization efforts by different market participants. Besides showing how market participants understand and use alternative data, we demonstrate that alternative data often undergo processes of prospecting and assetization. We further contend that the widespread embracement of alternative data in investment management and trading encourages a financialization process at the data level which raises new governance issues. (shrink)

Precision medicine is an emerging approach to treatment and disease prevention that relies on linkages between very large datasets of health information that is shared amongst researchers and health professionals. While studies suggest broad support for sharing precision medicine data with researchers at publicly funded institutions, there is reluctance to share health information with private industry for research and development. As the private sector is likely to play an important role in generating public benefits from precision medicine initiatives, it is (...) important to understand what the concerns are and how they might be mitigated. This study reports outcomes of a deliberative method of citizen engagement in Singapore that asked whether sharing precision medicine data with private industry would be permissible, and if so, under what circumstances. Findings from this citizens’ jury suggest sharing with industry would be permissible under certain conditions that are set out in nine recommendations. Implications of the recommendations and their underlying assumptions for policy decision makers are discussed. This study aligns with prior international studies which found conditional acceptance for data sharing with private industry, a public benefit requirement, specific reluctance to share with insurance companies and an emphasis on accountability and transparency to demonstrate trustworthiness. However, our results differ from prior studies in that opt-in consent did not dominate the deliberations as jurors were able to set it aside as an assumed prerequisite for participation in a precision medicine programme. (shrink)

In this paper, we examine the practice and promises of digital phenotyping. We build on work on the ‘data self’ to focus on a medical domain in which the value and nature of knowledge and relations with data have been played out with particular persistence, that of Alzheimer's disease research. Drawing on research with researchers and developers, we consider the intersection of hopes and concerns related to both digital tools and Alzheimer's disease using the metaphor of the ‘data shadow’. We (...) suggest that as a tool for engaging with the nature of the data self, the shadow is usefully able to capture both the dynamic and distorted nature of data representations, and the unease and concern associated with encounters between individuals or groups and data about them. We then consider what the data shadow ‘is’ in relation to ageing data subjects, and the nature of the representation of the individual's cognitive state and dementia risk that is produced by digital tools. Second, we consider what the data shadow ‘does’, through researchers and practitioners’ discussions of digital phenotyping practices in the dementia field as alternately empowering, enabling and threatening. (shrink)

This commentary elaborates on the ideas and projects outlined in this special issue, from a specifically sociological perspective. Much recent work in sociology proposes ‘methods mashups’ of ethnography and digital data/computational tools in different and diverse ways. However, typically, these have taken the form of applying the principles of ethnography to new domains and data types, as if ethnography itself is stable and immutable; that it has a universal set of methodological principles that unify ethnographic practice. Returning to anthropology is, (...) therefore, a useful way to extend our methodological thinking to consider what ethnography is and how it operates, and from there think more clearly about how it may be effectively combined with digital data/computational tools in an emerging ‘Computational Anthropology’. (shrink)

The introduction of digital technologies in collective actions seems to have transformed the dynamics of movement organizing and enabled divergent forms of protest organizing. While some studies emphasize “organizationless” organizing in which traditional organizational forms—social movements organizations and formal-bureaucratic structures—have been pushed into the margins, other studies showcase how traditional forms have assumed alternative features, for example, connective leadership and organizations with fluid boundaries. While existing research correctly points out the evolving organizing dynamics and forms in digital activism, few studies (...) have accounted for why digitally enabled protests take certain organizing forms over others among multiple modes of interaction between protesters and digital technologies. Using a case study of a protest campaign organized by Chinese American immigrants, this study illustrates why immigrant activists struggled to keep the campaign “organizationless” on WeChat, a China-based digital platform that afforded other forms of organizing over such an organizing mode. Building on the mechanism-based approach in social movement studies, the findings show that immigrant activists’ emotional–cognitive responses to the changing digital environments became the driving force behind the relational choices to maintain the protest “organizationless.” The study, therefore, may not only inform future studies to explore why certain structures of protest networks emerge and develop but also contribute to the mechanism-based approach by foregrounding emotional–cognitive mechanisms, which mediate environmental and relational mechanisms. (shrink)

This article examines discourses about mixed reality as a data-rich sensing technology – specifically, engaging with discourses of time as framed by developers, engineers and in corporate PR and marketing in a range of public facing materials. We focus on four main settings in which mixed reality is imagined to be used, and in which time was a dominant discursive theme – the development of mixed reality by big tech companies, the use of mixed reality for defence, mixed reality as (...) a technology for control of populations in civil society and mixed reality as a technology used in workplace settings. Across these settings, the broad narrative is that mixed reality technologies afford overwhelmingly positive benefits like efficiency and security through their capture, relay and rendition of data – affording a form of anticipatory power to the user. The framing of temporality, we argue, is underlain by social and political values, which represent certain interests, but leave others out in the imagination of mixed reality's technological advance. (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)

Recent buzzes around big data, data science and artificial intelligence portray a data-driven future for healthcare. As a response, Europe's key players have stimulated the use of big data technologies to make healthcare more efficient and effective. Critical Data Studies and Science and Technology Studies have developed many concepts to reflect on such overly positive narratives and conduct critical policy evaluations. In this study, we argue that there is also much to be learned from studying how professionals in the healthcare (...) field affectively engage with this strong European narrative in concrete big data projects. We followed twelve hospital-based big data pilots in eight European countries and interviewed 145 professionals between 2018 and 2020. In this study, we introduce the metaphor of dreams to describe how professionals link the big data promises to their own frustrations, ideas, values and experiences with healthcare. Our research answers the question: how do professionals in concrete data-driven initiatives affectively engage with European Union's data hopes in their ‘dreams’ – and with what consequences? We describe the dreams of being seen, of timeliness, of connectedness and of being in control. Each of these dreams emphasizes certain aspects of the grand narrative of big data in Europe, makes particular assumptions and has different consequences. We argue that including attention to these dreams in our work could help shine an additional critical light on the big data developments and stimulate the development of responsible data-driven healthcare. (shrink)

There has been a surge of recent interest in sociocultural diversity in machine learning research. Currently, however, there is a gap between discussions of measures and benefits of diversity in machine learning, on the one hand, and the broader research on the underlying concepts of diversity and the precise mechanisms of its functional benefits, on the other. This gap is problematic because diversity is not a monolithic concept. Rather, different concepts of diversity are based on distinct rationales that should inform (...) how we measure diversity in a given context. Similarly, the lack of specificity about the precise mechanisms underpinning diversity’s potential benefits can result in uninformative generalities, invalid experimental designs, and illicit interpretations of findings. In this work, we draw on research in philosophy, psychology, and social and organizational sciences to make three contributions: First, we introduce a taxonomy of different diversity concepts from philosophy of science, and explicate the distinct epistemic and political rationales underlying these concepts. Second, we provide an overview of mechanisms by which diversity can benefit group performance. Third, we situate these taxonomies of concepts and mechanisms in the lifecycle of sociotechnical machine learning systems and make a case for their usefulness in fair and accountable machine learning. We do so by illustrating how they clarify the discourse around diversity in the context of machine learning systems, promote the formulation of more precise research questions about diversity’s impact, and provide conceptual tools to further advance research and practice. (shrink)

We examined the relationship between political affiliation, perceptual estimates, and subjective judgements of disease prevalence and mortality across three chart types. An online survey exposed separate groups of participants to charts displaying COVID-19 data or COVID-19 data labeled ‘Influenza ’. Block 1 examined responses to cross-sectional mortality data ; results revealed that perceptual estimates comparing mortality in two countries were similar across political affiliations and chart types, while subjective judgements revealed a disease x political party interaction. Although Democrats and Republicans (...) provided similar proportion estimates, Democrats interpreted mortality to be higher than Republicans; Democrats also interpreted mortality to be higher for COVID-19 than Influenza. Block 2 examined responses to time series ; Democrats and Republicans estimated greater slopes for COVID-19 trend lines than Influenza lines ; subjective judgements revealed a disease x political party interaction. Democrats and Republicans indicated similar subjective rates of change for COVID-19 trends, and Democrats indicated lower subjective rates of change for Influenza than in any other condition. Thus, while Democrats and Republicans saw the graphs similarly in terms of percentages and line slopes, their subjective interpretations diverged. While we may see graphs of infectious disease data similarly from a purely mathematical or geometric perspective, our political affiliations may moderate how we subjectively interpret the data. (shrink)

Epidemiology is a field torn between practices of surveillance and methods of analysis. Since the onset of COVID-19, epidemiological expertise has been mostly identified with the first, as dashboards of case and mortality rates took centre stage. However, since its establishment as an academic field in the early 20th century, epidemiology’s methods have always impacted on how diseases are classified, how knowledge is collected, and what kind of knowledge was considered worth keeping and analysing. Recent advances in digital epidemiology, this (...) article argues, are not just a quantitative expansion of epidemiology’s scope, but a qualitative extension of its analytical traditions. Digital epidemiology is enabled by deep and digital phenotyping, the large-scale re-purposing of any data scraped from the digital exhaust of human behaviour and social interaction. This technological innovation is in need of critical examination, as it poses a significant epistemic shift to the production of pathological knowledge. This article offers a critical revision of the key literature in this budding field to underline the extent to which digital epidemiology is envisioned to redefine the classification and understanding of disease from the ground up. Utilising analytical tools from science and technology studies, the article demonstrates the disruptive expectations built into this expansion of epidemiological surveillance. Given the sweeping claims and the radical visions articulated in the field, the article develops a tentative critique of what I call a fantasy of pathological omniscience; a vision of how data-driven engineering seeks to capture and resolve illness in the world, past, present and future. (shrink)

Today's modern car is an assemblage of mechanical and digital components, of metal panels that comprise its structure and silicon chips that run its functions. Communication and information studies scholars have interrogated the problematic aspects of the programs that run those functions, revealing serious issues surrounding privacy and security, worker surveillance, and racial, gendered, and class-based bias. This article contributes to that work by taking a step back and asking about the issues inherent not in the software running on these (...) chips, but on the microchips themselves. Using the lens of “chipification”—the process by which a device is rendered capable of reading and processing data through the embedding of microchips—this article explores how the integration of microchips also involves processes that are often borne from, replicate, or create troubling power dynamics of their own. It takes light-duty passenger vehicles as a case study into how chipification is radically reshaping such processes as resource allocation, labor flows, and cultural practices around car manufacture, use, repair, and modification. By naming the process of chipification, this article allows researchers to identify and analyze the ways that integrating data processing capabilities into everyday devices is not a frictionless practice, but rather one that implicates a variety of power dynamics within these massive industries. (shrink)

Big data enables researchers to closely follow the behavior of large groups of individuals by using high-frequency digital traces. However, these digital traces often lack context, and it is not always clear what is measured. In contrast, data from ethnographic fieldwork follows a limited number of individuals but can provide the context often lacking from big data. Yet, there is an under-explored potential in combining ethnographic data with big data and other digital data sources. This paper presents ways that quantitative (...) research designs can combine big data and ethnographic data and account for the synergies that such combinations can provide. We highlight the differences and similarities between ethnographic data and big data, focusing on the three dimensions: individuals, depth of information, and time. We outline how ethnographic data can validate big data by providing a “ground truth” and complement it by giving a “thick description.” Further, we lay out ways that analysis carried out using big data could benefit from collaboration with ethnographers, and we discuss the potential within the fields of machine learning and causal inference. (shrink)

In this exploratory study, we examine political polarization regarding the online discussion of the COVID-19 pandemic. We use data from Reddit to explore the differences in the topics emphasized by different subreddits according to political ideology. We also examine whether there are systematic differences in the credibility of sources shared by the subscribers of subreddits that vary by ideology, and in the tendency to share information from sources implicated in spreading COVID-19 misinformation. Our results show polarization in topics of discussion: (...) the Trump, White House, and economic relief topics are statistically more prominent in liberal subreddits, and China and deaths topics are more prominent in conservative subreddits. There are also significant differences between liberal and conservative subreddits in their preferences for news sources. Liberal subreddits share and discuss articles from more credible news sources than conservative subreddits, and conservative subreddits are more likely than liberal subreddits to share articles from sites flagged for publishing COVID-19 misinformation. (shrink)

This special theme of Big Data & Society explores connections, relationships, and tensions that coalesce around data, power, and racial formation. This collection of articles and commentaries builds upon scholarly observations of data substantiating and transforming racial hierarchies. Contributors consider how racial projects intersect with interlocking systems of oppression across concerns of class, coloniality, dis/ability, gendered difference, and sexuality across contexts and jurisdictions. In doing so, this special issue illuminates how data can both reinforce and challenge colorblind ideologies as well (...) as how data might be mobilized in support of anti-racist movements. (shrink)

According to Clifford Geertz, the purpose of anthropology is not to explain culture but to explicate it. That should cause us to rethink our relationship with machine learning. It is, we contend, perfectly possible that machine learning algorithms, which are unable to explain, and could even be unexplainable themselves, can still be of critical use in a process of explication. Thus, we report on an experiment with anthropological AI. From a dataset of 175K Facebook comments, we trained a neural network (...) to predict the emoji reaction associated with a comment and asked a group of human players to compete against the machine. We show that a) the machine can reach the same accuracy as the players, b) it fails in roughly the same ways as the players, and c) easily predictable emoji reactions tend to reflect unambiguous situations where interpretation is easy. We therefore repurpose the failures of the neural network to point us to deeper and more ambiguous situations where interpretation is hard and explication becomes both necessary and interesting. We use this experiment as a point of departure for discussing how experiences from anthropology, and in particular the tension between formalist ethnoscience and interpretive thick description, might contribute to debates about explainable AI. (shrink)

Academic and public debates are increasingly concerned with the question whether and how algorithmic decision-making may reinforce social inequality. Most previous research on this topic originates from computer science. The social sciences, however, have huge potentials to contribute to research on social consequences of ADM. Based on a process model of ADM systems, we demonstrate how social sciences may advance the literature on the impacts of ADM on social inequality by uncovering and mitigating biases in training data, by understanding data (...) processing and analysis, as well as by studying social contexts of algorithms in practice. Furthermore, we show that fairness notions need to be evaluated with respect to specific outcomes of ADM systems and with respect to concrete social contexts. Social sciences may evaluate how individuals handle algorithmic decisions in practice and how single decisions aggregate to macro social outcomes. In this overview, we highlight how social sciences can apply their knowledge on social stratification and on substantive domains of ADM applications to advance the understanding of social impacts of ADM. (shrink)

People's sense of being subject to digital dataveillance can cause them to restrict their digital communication behavior. Such a chilling effect is essentially a form of self-censorship in everyday digital media use with the attendant risks of undermining individual autonomy and well-being. This article combines the existing theoretical and limited empirical work on surveillance and chilling effects across fields with an analysis of novel data toward a research agenda. The institutional practice of dataveillance—the automated, continuous, and unspecific collection, retention, and (...) analysis of digital traces—affects individual behavior. A mechanism-based causal model based on the theory of planned behavior is proposed for the micro level: An individual's increased sense of dataveillance causes their subjective probability assigned to negative outcomes of digital communication behavior to increase and attitudes toward this communication to become less favorable, ultimately decreasing the intention to engage in it. In aggregate and triggered through successive salience shocks such as data scandals, dataveillance is accordingly hypothesized to lower the baseline of free digital communication in a society through the chilling effects mechanism. From the developed theoretical model, a set of methodological consequences and questions for future studies are derived. (shrink)

Effective social data governance rests on a bedrock of social support. Without securing trust from the populace whose information is being collected, analyzed, and deployed, policies on which such data are based will be undermined by a lack of public confidence. The COVID-19 pandemic has accelerated digitalization and datafication by governments for the purposes of contact tracing and epidemiological investigation. However, concerns about surveillance and data privacy have stunted the adoption of such contact-tracing initiatives. This commentary analyzes Singapore's contact-tracing initiative (...) to uncover the reasons for public resistance and efforts by the state to address them. The government's contact-tracing program encompassing its proprietary TraceTogether app and physical token initially triggered vociferous public criticisms of Big Brother style surveillance. Using a dialogic communication framework, we analyze the TraceTogether initiative to interrogate the communicative strategies that were used to overcome public resistance. We argue that these strategies reflect a top-down approach that prioritizes transactional dissemination of information, in line with Singapore's technocratic stance toward governance. We further assert that such communicative tactics represent missed opportunities to foster public confidence in social data governance through greater trust building. We propose solutions for more dialogic communicative forms that build trust, so that officials can develop a sound understanding of the public concerns, increase the level of public engagement, and incorporate public feedback into policies that govern data use. (shrink)

The algorithmic accountability literature to date has primarily focused on procedural tools to govern automated decision-making systems. That prescriptive literature elides a fundamentally empirical question: whether and under what circumstances, if any, is the use of algorithmic systems to make public policy decisions perceived as legitimate? The present study begins to answer this question. Using factorial vignette survey methodology, we explore the relative importance of the type of decision, the procedural governance, the input data used, and outcome errors on perceptions (...) of the legitimacy of algorithmic public policy decisions as compared to similar human decisions. Among other findings, we find that the type of decision—low importance versus high importance—impacts the perceived legitimacy of automated decisions. We find that human governance of algorithmic systems increases perceptions of the legitimacy of algorithmic decision-making systems, even when those decisions are likely to result in significant errors. Notably, we also find the penalty to perceived legitimacy is greater when human decision-makers make mistakes than when algorithmic systems make the same errors. The positive impact on perceived legitimacy from governance—such as human-in-the-loop—is greatest for highly pivotal decisions such as parole, policing, and healthcare. After discussing the study’s limitations, we outline avenues for future research. (shrink)

In recent years, universities have been urged to restructure and re-evaluate their ability to trace and monitor their students as the “smart campus” is being built upon datafication, while networked apps and sensors serve as the means through which its constituents are connected and governed. This paper advances a dialectical and communication-centered approach to the Internet of Things campus ecosystem and provides an empirical investigation into the tensions experienced by students and the ways that these students envision alternative practices that (...) support their digital engagement. Drawing upon student focus group interviews in a large American research and innovation intensive university, dialectical tensions identified include convenience–annoyance, integration–independence, and safety–insecurity, brought upon by students’ ongoing and prospective negotiations with Internet of Things. Furthermore, in a bid to understand students’ alternative data imaginaries, this project examined students’ preferred Internet of Things-related communication practices with campus digital application platforms, analog and older forms of digital media, as well as in-person interactions with traditional authorities within classroom and group settings. Finally, this contribution presents a discussion of the findings for theory and praxis, particularly for smart campus innovation and social data governance, in terms of potential growing challenges involving complexifying student privacy concerns, data normalization and coercion, and tertiary digital divides and inequalities. (shrink)

How corporations surveil and influence consumers using big data tools is a major area of research and public debate. However, few studies explore it in relation to physicians in the USA, even though they have been surveilled and targeted by the pharmaceutical industry since at least the 1950s. Indeed, in 2010, concerns about the pharmaceutical industry's undue influence led to the passing of the Physician Sunshine Act, a unique piece of transparency legislation that requires companies to report their financial ties (...) to physicians and teaching hospitals in a public database. This article argues that while the Sunshine Act has clearly helped expose important commercial influences on both prescribing and the scale of industry involvement with physicians, it has also, paradoxically, fuelled further commercial surveillance and marketing. The article casts new light on innovative pharmaceutical marketing approaches and the key role of data brokers and analytics companies in the identification, targeting, managing, and surveillance of physicians. We place this analysis within the political economies of the pharmaceutical industry, surveillance-based marketing, and transparency, and argue that policies to promote increased transparency must be tightly tied to policies that impede the commodification and use of transparency data for surveillance and marketing purposes. (shrink)

In this study, we build on a recent social data scientific mapping of Danish environmentalist organizations and activists during the COVID-19 lockdown in order to sketch a distinct genre of digital social research that we dub a quali-quantitative micro-history of ideas in real-time. We define and exemplify this genre by tracing and tracking the single political idea and activist slogan of grøn genstart across Twitter and other public–political domains. Specifically, we achieve our micro-history through an iterative and mutual attuning between (...) computational and netnographic registers and techniques, in ways that contribute to the nascent field of computational anthropology. By documenting the serial ways in and different steps through which our inquiry was continually fed and enhanced by crossing over from ethnographic observation to computational exploration, and vice versa, we offer up our grøn genstart case account as exemplary of wider possibilities in this line of inquiry. In particular, we position the genre of micro-history of ideas in real-time within the increasingly wide and heterogeneous space of digital social research writ large, including its established concerns with ‘big and broad’ social data, the repurposing of computational ‘interface’ techniques for socio-cultural research, as well as diverse aspirations for deploying digital data within novel combinations of qualitative and quantitative methods. (shrink)

This article introduces the methodology of the ‘Ontology Explorer’, a semantic method and JavaScript-based open-source tool to analyse data models underpinning information systems. The Ontology Explorer has been devised and developed by the authors, who recognized a need to compare data models collected in different formats and used by diverse systems. The Ontology Explorer is distinctive firstly because it supports analyses of information systems that are not immediately comparable and, secondly, because it systematically and quantitatively supports discursive analysis of ‘thin’ (...) data models – also by detecting differences and absences through comparison. When applied to data models underpinning systems for population management, the Ontology Explorer enables the apprehension of how people are ‘inscribed’ in information systems: which assumptions are made about them, and which possibilities are excluded by design. The Ontology Explorer thus constitutes a methodology to capture authorities’ own imaginaries of populations and the ‘scripts’ through which they enact actual people. Furthermore, the method allows the comparison of scripts from diverse authorities. This is exemplified by illustrating its functioning with information systems for population management deployed at the European border. Our approach integrates a number of insights from early infrastructure studies and extends their methods and analytical depth to account for contemporary data infrastructures. By doing so, we hope to trigger a systematic discussion on how to extend those early methodical innovations at the semantic level to contemporary developments in digital methods. (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)

Some recent uses of artificial intelligence for facial recognition, evaluating resumes, and sorting photographs by subject matter have revealed troubling disparities in performance or impact based on the demographic traits of subject populations. These disparities raise pressing questions about how using artificial intelligence can work to promote justice or entrench injustice. Political theorists and philosophers have developed nuanced vocabularies and theoretical frameworks for understanding and adjudicating disputes about what justice requires and what constitutes injustice. The interdisciplinary community committed to understanding (...) and conscientiously using big data could benefit from this work. Thus, in the spirit of encouraging cross-disciplinary dialogue and collaboration, this piece examines contemporary scholarship in political theory and philosophy to illustrate some of the vocabularies and frameworks political theorists and philosophers have developed for thinking about justice and injustice. It then draws on these frameworks to illuminate how the use of artificial intelligence can implicate questions of justice, with a focus on institutional discrimination, structural injustice, and epistemic injustice. Ultimately, the piece argues that the use of artificial intelligence—far from representing a decision to take power out of human hands—represents a novel way of harnessing human power, making questions of justice central to its conscientious undertaking. (shrink)

The size and variation in both meaning-making and populations that characterize much contemporary text data demand research processes that support both discovery, interpretation and measurement. We assess one dominant strategy within the social sciences that takes a computer-led approach to text analysis. The approach is coined computational grounded theory. This strategy, we argue, relies on a set of unwarranted assumptions, namely, that unsupervised models return natural clusters of meaning, that the researcher can understand text with limited immersion and that indirect (...) validation is sufficient for ensuring unbiased and precise measurement. In response to this criticism, we develop a framework that is computer assisted. We argue that our reformulation of computational grounded theory better aligns with the principles within grounded theory, anthropological theory generation and ethnography. (shrink)

Big data technologies are entering the world of ageing computer systems running critical infrastructures. These innovations promise to afford rapid Internet connectivity, remote operations or predictive maintenance. As legacy critical infrastructures were traditionally disconnected from the Internet, the prospect of their modernisation necessitates an inquiry into cyber security and how it intersects with traditional engineering requirements like safety, reliability or resilience. Looking at how the adoption of big data technologies in critical infrastructures shapes understandings of risk management, we focus on (...) a specific case study from the cyber security governance: the EU Network and Information Systems Security Directive. We argue that the implementation of Network and Information Systems Security Directive is the first step in the integration of safety and security through novel risk management practices. Therefore, it is the move towards legitimising the modernisation of critical infrastructures. But we also show that security risk management practices cannot be directly transplanted from the safety realm, as cyber security is grounded in anticipation of the future adversarial behaviours rather than the history of equipment failure rates. Our analysis offers several postulates for the emerging research agenda on big data in complex engineering systems. Building on the conceptualisations of safety and security grounded in the materialist literature across Science and Technology Studies and Organisational Sociology, we call for a better understanding of the ‘making of’ technologies, standardisation processes and engineering knowledge in a quest to build safe and secure critical infrastructures. (shrink)

The administrative reform of the datafied public administration places great emphasis on the classification, control, and prediction of citizen behavior and therefore has the potential to significantly impact citizen–state relations. There is a growing body of literature on data-oriented activism which aims to resist and counteract existing harmful data practices. However, little is known about the processes, policies, and political-economic structures that make datafication possible. There is a distinct research gap on situated and context-specific empirical research, which critically interrogates the (...) premises, interests, and agendas of data-driven public administration and how stakeholders can impact them. This paper therefore studies the conditions of participation in public administration datafication. It asks the overall research question of how citizens are problematized and included in policy and practitioner discourse in the datafication of public administration. The paper takes Norway as its case and applies Cardullo and Kitchin’s scaffold of smart citizen participation at the system level. It makes use of a unique empirical insight into the field, consisting of a survey, interviews, and an extensive document analysis. Unexpectedly, we find that citizens and civil society are rarely engaged in this administrative reform. Instead, we identify a paternalistic, top-down, technocratic approach where the context, values, and agendas of datafication are obscured from the citizen. (shrink)

As natural language processing tools powered by big data become increasingly ubiquitous, questions of how to design, develop, and manage these tools and their impacts on diverse populations are pressing. We propose utilizing the concept of linguistic justice—the realization of equitable access to social and political life regardless of language—to provide a framework for examining natural language processing tools that learn from and use human language data. To support linguistic justice, we argue that natural language processing tools must be examined (...) not only from the perspective of a privileged, majority language user, but also from the perspectives of minoritized language users. Considering such perspectives can help to surface areas in which the data used within natural language processing tools may be working against linguistic justice by failing to provide access to information, services, or opportunities in users’ language of choice, underperforming for certain linguistic groups, or advancing harmful stereotypes that can lead to negative life outcomes for members of marginalized groups. At the same time, this framework can help to illuminate ways that these shortcomings can be addressed and allow us to use inclusive language data and approaches to leverage natural language processing technologies that advance linguistic justice. (shrink)

The Trump Administration's governance of COVID-19 racial health disparities data has become a key front in the viral war against the pandemic and racial health injustice. In this paper, I analyze how the COVID-19 pandemic joins an already ongoing racial spectacle and system of structural gaslighting organized around “racial health disparities” in the United States and globally. The field of racial health disparities has yet to question the domain assumptions that uphold its field of investigation; as a result, the entire (...) reform program called for by racial health disparities science is already featured on the menu of the white supremacist power structure. The societal infrastructure that produces scientific knowledge about patterns of health and disease in the human population needs to confront its structural position as part of the racial spectacle organized around racial health disparities in the United States. This paper offers an interpretation of racial antimatter to explain why the data will not save us in the COVID-19 pandemic, drawing on articulations of racial spectacle and structural gaslighting within critical race theory and Afropessimist thought. By positioning events in the COVID-19 pandemic together within the same racially speculative frame, I show how the collection of racial health disparities data came up against white supremacists’ political ambitions in a time-space where the demand for human life to matter and the iterative regeneration of racial antimatter collided. This paper highlights the need for ongoing analysis of the unfolding and future spectacles organized around racial health disparities. (shrink)

Scholars have focused on algorithms used during sentencing, bail, and parole, but little work explores what we term “carceral algorithms” that are used during incarceration. This paper is focused on the Pennsylvania Additive Classification Tool used to classify prisoners’ custody levels while they are incarcerated. Algorithms that are used during incarceration warrant deeper attention by scholars because they have the power to enact the lived reality of the prisoner. The algorithm in this case determines the likelihood a person would endure (...) additional disciplinary actions, can complete required programming, and gain experiences that, among other things, are distilled into variables feeding into the parole algorithm. Given such power, examining algorithms used on people currently incarcerated offers a unique analytic view to think about the dialectic relationship between data and algorithms. Our examination of the PACT is two-fold and complementary. First, our qualitative overview of the historical context surrounding PACT reveals that it is designed to prioritize incapacitation and control over rehabilitation. While it closely informs prisoner rehabilitation plans and parole considerations, it is rooted in population management for prison securitization. Second, on analyzing data for 146,793 incarcerated people in PA, along with associated metadata related to the PACT, we find it is replete with racial bias as well as errors, omissions, and inaccuracies. Our findings to date further caution against data-driven criminal justice reforms that rely on pre-existing data infrastructures and expansive, uncritical, data-collection routines. (shrink)

Healthcare provision, like many other sectors of society, is undergoing major changes due to the increased use of data-driven methods and technologies. This increased reliance on big data in medicine can lead to shifts in the norms that guide healthcare providers and patients. Continuous critical normative reflection is called for to track such potential changes. This article presents the results of an interview-based study with 20 German and Swiss experts from the fields of medicine, life science research, informatics and humanities (...) of digitalisation. The aim of the study was to explore expert opinions regarding current challenges and opportunities related to data-driven medicine and medical research and to provide a methodological framework for empirically grounded, continuous normative reflection. To this end, we developed a heuristic tool to map and structure empirical findings for normative analysis. Using this tool, our interview material points to a polarisation between individualistic and collectivistic orientated argumentations among experts. The study shows that a multilevel analysis is required to deal with complex normative implications of data-driven approaches in medical research and healthcare. (shrink)

Poor air quality is a pressing global challenge contributing to adverse health impacts around the world. In the past decade, there has been a rapid proliferation of air quality information delivered via sensors, apps, websites or other media channels in near real-time and at increasingly localized geographic scales. This paper explores the growing emphasis on self-monitoring and digital platforms to supply informational interventions for reducing pollution exposures and improving health outcomes at the individual level. It presents a technological case study (...) that characterizes emerging air quality information communication mechanisms, or ‘AQ channels’, while drawing upon examples throughout the literature. The questions are posed: which air quality channels are ‘freely’ available to individuals in London, UK, and when and where are they accessed? Digital trace data and metadata associated with 54 air quality channels are synthesized narratively and graphically. Results reveal air quality channels derive air pollution estimates using common data sources, display disparate messaging, adopt variable geographic scales for reporting ‘readings’ and maintain psychosocial barriers to access and adoption of exposure-reducing behaviours. The results also point to a clear association between the publication of a high-profile news article about air pollution and increased air quality channel access. These findings illuminate a need for greater transparency around how air quality channels generate personalized air pollution exposure estimates and tailor messaging. The paper concludes by calling for air quality channel developers to exercise co-creative methods that can support sustainable, democratic data and knowledge production around air quality, while critically approaching disproportionate patterns of both pollution and information exposure. (shrink)

A major challenge with the increasing use of Artificial Intelligence applications is to manage the long-term societal impacts of this technology. Two central concerns that have emerged in this respect are that the optimized goals behind the data processing of AI applications usually remain opaque and the energy footprint of their data processing is growing quickly. This study thus explores how much people value the transparency and environmental sustainability of AI using the example of personal AI assistants. The results from (...) a choice-based conjoint analysis with a sample of more than 1.000 respondents from Germany indicate that people hardly care about the energy efficiency of AI; and while they do value transparency through explainable AI, this added value of an application is offset by minor costs. The findings shed light on what kinds of AI people are likely to demand and have important implications for policy and regulation. (shrink)

Digital organizations form part of the new wave of blockchain technologies, following Bitcoin and related cryptocurrencies. “Utopia ofion” offers an analysis of the utopian promise of digital organizations through a reading of one such project, Colony. We provide a critique of the ideology of Colony's white paper, supplemented by readings of pages from its website, as a member of a genre of texts that promote their products through seemingly neutral, technical descriptions. Colony's texts suggest an abstract, contextless and scaleless organizational (...) solution—powered by smart contracts on a blockchain—that, according to its proponents, might be applied to any social situation, from small firm to state-level governance. For its users, this organization combines a promise of sovereignty removed from that of the state, as well as implied financial returns. Our reading of Colony echoes the critiques of scholars arguing that cyberlibertarianism is a dominant politic of blockchain technologies. Furthermore, drawing on critiques of code as law and the elision of the social in smart contracts, we argue that Colony's vision presents a model of technical organization that substitutes for the state in the context of waning popular sovereignty. We ultimately suggest an understanding of digital organizations reminiscent of the settler colonial situation: the assumption of an empty social space to be filled, and the promise of sovereignty and riches for those occupying it. Analysis of these logics is relevant as hype increases around non-fungible tokens, Web3, and the corporate metaverse as well as data practices more widely. (shrink)

Data visualization researchers and designers have explored a range of approaches to ensure that non-expert audiences understand and derive value from their work. Using anthropomorphized data graphics—or anthropographics—is one strategy that can help create a connection between data and audiences. Anthropographics have been defined as “visualizations that represent data about people in a way that is intended to promote prosocial feelings or prosocial behavior.” However, during the SARS-CoV-2 pandemic, anthropographics were used in data visualizations that had an expanded range of (...) rhetorical goals beyond promoting prosocial feelings and behavior—for instance, informing people about the pandemic, persuading them to adopt certain behaviors, or memorializing those killed by the virus. In particular, anthropographics were used in visualized simulations to model possible futures for audiences, showing the spread and impact of the virus in various scenarios. These simulations used anthropomorphizing strategies in text as well as in graphics, along with interactive options that enabled audiences to explore personal connections with the data. As demonstrated through a close reading of several of these COVID-19 simulations, anthropographics can be viewed holistically as a design strategy that incorporates text and interactivity as well as graphical marks in representing data. Findings from this analysis suggest several additions to the design space for anthropographics. (shrink)

This article seeks to address current debates comparing polls and opinion mining as empirically based figuration models of public opinion in the light of in-depth intellectual debates on the role and nature of public opinion that began after the French Revolution and the controversy over public opinion spurred by the invention of polls. Issues of historical quantification and re-conceptualisation of public opinion are addressed in four parts. The first summarises the history of the rise and fall of the concept of (...) public opinion. The second re-examines the key controversies in the debates on the theoretical, empirical and social implications and consequences of the invention of polling. The third part scrutinises the datafication of public opinion that started with polling industry and continues in the age of big data and data mining. The final section discusses the controversial potentials of opinion-mining technology and suggests ways in which social scientists could critically respond to the big data and opinion-mining challenges in order to reintegrate the ideas of publicness, the public and public sphere into public opinion research. (shrink)

Despite the immense societal importance of ethically designing artificial intelligence, little research on the public perceptions of ethical artificial intelligence principles exists. This becomes even more striking when considering that ethical artificial intelligence development has the aim to be human-centric and of benefit for the whole society. In this study, we investigate how ethical principles are weighted in comparison to each other. This is especially important, since simultaneously considering ethical principles is not only costly, but sometimes even impossible, as developers (...) must make specific trade-off decisions. In this paper, we give first answers on the relative importance of ethical principles given a specific use case—the use of artificial intelligence in tax fraud detection. The results of a large conjoint survey suggest that, by and large, German respondents evaluate the ethical principles as equally important. However, subsequent cluster analysis shows that different preference models for ethically designed systems exist among the German population. These clusters substantially differ not only in the preferred ethical principles but also in the importance levels of the principles themselves. We further describe how these groups are constituted in terms of sociodemographics as well as opinions on artificial intelligence. Societal implications, as well as design challenges, are discussed. (shrink)

In this paper, we explore the methodologies underpinning two participatory research collaborations with Australian non-profit organisations that aimed to build data capability and social benefit in data use. We suggest that studying and intervening in data practices in situ, that is, in organisational data settings expands opportunities for improving the social value of data. These situated and collaborative approaches not only address the ‘expertise lag’ for non-profits but also help to realign the potential social value of organisational data use. We (...) explore the relationship between data literacy, data expertise and data capability to test the idea that collaborative work with non-profit organisations can be a practical step towards addressing data equity and generating data-driven social outcomes. Rather than adopting approaches to data literacy that focus on individuals – or ideal ‘data citizens’ – we target the organisation-wide data settings, goals and practices of the non-profit sector. We conclude that participatory methods can embed social value-generating data capability where it can be sustained at an organisational level, aligning with community needs to promote collaborative data action. (shrink)

Ample scholarship demonstrates that data-intensive technologies have the capacity to cause serious harm and that their developers are obliged to address ethics in their work. This ethnographic paper tells the story of data scientists attempting to instantiate a carefully considered ethical vision into a data infrastructure while balancing competing priorities, negotiating divergent interests, and wrestling with contrasting values. I use their story to develop the concept of “ethical abduction,” which I characterize as an exemplary process by which actors can intentionally (...) and systematically address ethical issues that arise during their day-to-day actions by making decisions with consideration for a foundational ethical worldview. It entails tacking back and forth between divergent but complementary ways of thinking: between establishing ideals and making decisions given practical constraints; between understanding historical context and anticipating future consequences; between acknowledging structural dependencies and accepting responsibility for moral agency. (shrink)

We have identified an emerging tool being used by the UK government across a range of public bodies in the service of public policy - the online targeted advertising infrastructure and the practices, consultancy firms, and forms of expertise which have grown up around it. This reflects an intensification and adaptation of a broader ‘behavioural turn’ in the governmentality of the UK state and the increasing sophistication of everyday government communications. Contemporary UK public policy is fusing with the powerful tools (...) for behaviour change created by the platform economy. Operational data and associated systems of classification and profiling from public bodies are being hybridised with traditional consumer marketing profiles and then ‘projected’ onto the classification systems of the targeted advertising infrastructures. This is not simply a case of algorithms being used for sorting, surveilling, and scoring; rather this suggests that targeted interventions in the cultural and behavioural life of communities are now a core part of governmental power which is being algorithmically-driven, in combination with influencer networks, traditional forms of messaging, and frontline operational practices. We map these uses and practices of what we describe as the ‘Surveillance Influence Infrastructure’, identifying key ethical issues and implications which we believe have yet to be fully investigated or considered. What we find particularly striking is the coming-together of two separate structures of power - the governmental turn to behaviourism and prevention on one hand, and the infrastructures of targeting and influence on the other. We theorise this as a move beyond ‘nudge’ or ‘behavioural science’ approaches, towards a programme which we term ‘influence government’. (shrink)

There is increasing criticism on the use of big data and algorithms in public governance. Studies revealed that algorithms may reinforce existing biases and defy scrutiny by public officials using them and citizens subject to algorithmic decisions and services. In response, scholars have called for more algorithmic transparency and regulation. These are useful, but ex post solutions in which the development of algorithms remains a rather autonomous process. This paper argues that co-design of algorithms with relevant stakeholders from government and (...) society is another means to achieve responsible and accountable algorithms that is largely overlooked in the literature. We present a case study of the development of an algorithmic tool to estimate the populations of refugee camps to manage the delivery of emergency supplies. This case study demonstrates how in different stages of development of the tool—data selection and pre-processing, training of the algorithm and post-processing and adoption—inclusion of knowledge from the field led to changes to the algorithm. Co-design supported responsibility of the algorithm in the selection of big data sources and in preventing reinforcement of biases. It contributed to accountability of the algorithm by making the estimations transparent and explicable to its users. They were able to use the tool for fitting purposes and used their discretion in the interpretation of the results. It is yet unclear whether this eventually led to better servicing of refugee camps. (shrink)

Digitization and datafication of public space have a significant impact on how cities are developed, governed, perceived and used. As technological developments are based upon political decisions, which impact people’s everyday lives, and from which not everyone benefits or suffers equally, we argue that ‘the smart city’ should be part of continuous public debate; that it should be considered and treated as a social problem. Through nine focus groups, we invited respondents to explore and discuss instances and dilemmas of the (...) smart city. We investigated which interpretative repertoires they used to frame the smart city as a social and actionable problem. Following Blumer's and Gamson's theories on the social construction of problems and on collective action frames, we assessed respondents’ discursive interpretations and their subjective construction of their senses of injustice, agency and identity regarding this subject. We find that – in the context of the city of Rotterdam in The Netherlands – citizens do not experience and consider the smart city as a social and actionable problem. Although they do associate the technological development of smart cities with potential threats, this does not change or constrain their sense of ‘actionability’, nor their behaviour, as they consider themselves to be powerless individuals regarding what, in their eyes, is a complex, elusive and inevitable situation they are confronted with. Strikingly, rather than specifically and contextually reflecting on smart city issues, respondents tended to express their concerns in the more general context of digital and data technologies invading everyday life. (shrink)

Social media platforms take on increasingly big roles in political advertising. Microtargeting techniques facilitate the display of tailored advertisements to specific subsegments of society. Scholars worry that such techniques might cause political information to be displayed to only very small subgroups of citizens. Or that targeted communication about policy could make the mandate of elected representatives more challenging to interpret. Policy information in general and pledges, in particular, have received much scientific scrutiny. Scholars have focused largely on party manifestos, but (...) policy information and pledges communicated via online advertisements offer a new arena with new dynamics. This study uses Facebook’s ad library to describe how Dutch political campaigns advertise policy information and pledges in the run-up to the 2019 European Elections. The results show that much policy information is displayed to small subsegments of society. These findings provide evidence for concerns about pledge obfuscation, voter manipulation and mandate interpretation. (shrink)

Smart cities are now an established context in which data and digital technologies shape urban politics. Despite increased scholarly focus on algorithmic governance, smart cities and their data production still heavily rely on human labor, raising questions about how that labor is recruited and the implications of different recruitment strategies. In this paper, we illuminate the relations and practices mobilized to recruit the labor required to produce, analyze, and enact data that produce smart cities. We argue that smart cities recruit (...) such digital labor by producing and circulating moral values and sentiments to claim that such participation is a social good. In this article we draw on a 6-year ongoing project in Calgary, Canada to explore how these “moral economies” underwrite smart city ecosystems. We explore three projects related to data and digital labor in the Calgary smart city: a wearable technology collaborative project, a civic hacking group, and the community social media platform Nextdoor. We suggest that moral economies of smart cities signal a new juncture between urban planning and profiting from data, with the potential for creating new socio-political risks. These moral economies signal a shift toward a “new spirit of capitalism” in which labor is managed through indirect persuasion rather than direct compulsion and mandate. (shrink)

Research has shown that paternal involvement positively impacts on child health and development. We aimed to develop a conceptual model of dimensions of fatherhood, identify and categorise methods used for linking fathers with their children in administrative data, and map these methods onto the dimensions of fatherhood. We carried out a systematic scoping review to create a conceptual framework of paternal involvement and identify studies exploring the impact of paternal exposures on child health and development outcomes using administrative data. We (...) identified four methods that have been used globally to link fathers and children in administrative data based on family or household identifiers using address data, identifiable information about the father on the child's birth registration, health claims data, and Personal Identification Numbers. We did not identify direct measures of paternal involvement but mapping linkage methods to the framework highlighted possible proxies. The addition of paternal National Health Service numbers to birth notifications presents a way forward in the advancement of fatherhood research using administrative data sources. (shrink)

Since 2020, many countries worldwide have deployed digital contact tracing programs that rely on a range of digital sensors in the city to locate and map the routes of viral spread. Many critical commentaries have raised concerns about the privacy risks and trustworthiness of these programs. Extending these analyses, this paper opens up a different line of questioning that goes beyond privacy-centered single-axis critique of surveillance by considering digital contact tracing symptomatic of the broader changes in modes of urban governance (...) that renders our cities traceable, knowable, and governable through data. Based on archival and real-time analysis of South Korean national and local COVID-19 dashboards, online forums, and interviews with South Korean public health practitioners, this paper offers a sociotechnical analysis of digital contact tracing that looks at the various intersections of state-political, bio-political, and techno-political power dynamics. In contrast to popular narratives that attributed the success of the Korean approach to digital contact tracing to its collectivist culture and smart city infrastructures, this paper suggests that the case can be better understood by looking at both the macro-level shift in the forms of governance that takes on a spatialized and networked character and the micro-level formation of moral responsibility that shape one's conduct as a health and safety-conscious citizens. As the latest realization of the expanding regime of traceability in digital/urban governance, the development of digital contract tracing is seen to parallel with concurrent changes occurring in multiple domains of life including knowledge production, cultural memory, and individual subjectivity. (shrink)