Journal Description
Big Data & Society (BD&S) is an Open Access peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal’s key purpose is to provide a space for connecting debates about the emerging field of Big Data practices … |
This journal is a member of the Committee on Publication Ethics (COPE).
Journal Feed
- by Daniel Leix PalumboBig Data & Society, Volume 11, Issue 4, October-December 2024. The voice biometrics industry is promised today as a new center of digital innovation. Tech companies and state agencies are massively investing in speech recognition and analysis systems, pushed by the belief that the acoustics of voice contain unique individual characteristics to convert into information and value through artificial intelligence. This article responds to this current development by exploring the under-researched datafication of the auditory realm to reveal how the sound of voice is emerging as a site for identity construction by both states and corporations. To do so, we […]
- by Dragoș M ObrejaBig Data & Society, Volume 11, Issue 4, October-December 2024. Political and moral/religious contents are increasingly popular on TikTok, and the concerns associated with them create the premises for a re-exploration of the user–machine agency negotiation. Using algorithmic awareness as a process, this research examines the relationship between users’ awareness of the TikTok algorithm and the main concerns associated with content that conveys political or moral/religious tenets. A survey of 329 Romanian students showed that greater algorithm awareness influences positive attitudes toward algorithms, but significantly stronger positive effects are observed between awareness and the two mediators related to political and […]
- by Terrence Ting-Yen ChenBig Data & Society, Volume 11, Issue 4, October-December 2024. What kind of “democracy” do new government-led digital initiatives facilitate? This paper discusses the issue by investigating the open government data policy in Taiwan in the 2010s, asking whether the policy encouraged “strong democracy.” Using interviews, written records, and an analysis of platform design, I argue that the implementation of Taiwan's open data policy has not institutionalized the engagement of civil society groups or ordinary citizens in government decision-making processes, which is at odds with the claims that open government data encourages “strong democracy.” Instead, open government data in Taiwan […]
- by Itzelle Medina-PereaBig Data & Society, Volume 11, Issue 4, October-December 2024. In this paper, we bring together the concepts of data valences and data journeys to examine how ideational and material factors work together to shape the movement of health data from the UK healthcare sector to universities for reuse in research. Specifically, we focus on the interaction of university-based researchers’ constructs about data with the material conditions of health data circulation in the UK and how these dynamics drive greater circulation of health data through the data sharing infrastructure. Building on our empirical research, we identify four data valences or […]
- by Nada AkrouhBig Data & Society, Volume 11, Issue 4, October-December 2024. In recent years, citizen engagement in policy and research has gained considerable momentum. In the healthcare domain, patient narratives, through various mediums, have emerged as a valuable source of insight into the experiences of patients and the healthcare system. Recognizing the value of such textual data, diverse analytical methods have been developed, spanning from text mining to narrative analysis. This article presents experiments that combine computational methods, qualitative methods and citizen science for analyzing patients’ stories. In this article, we reflect on two experiments in which we combined these approaches, […]
- by Longxuan ZhaoBig Data & Society, Volume 11, Issue 4, October-December 2024. This study employs semistructured interviews and algorithmic ethnography to explore how algorithmic shadowbans have been used to moderate content related to Chinese gay men and achieve targeted algorithmic governance. Through a multimethod approach combining both thematic analysis and discourse analysis, this study claims that algorithms impose seemingly tolerant but actually restrictive shadowbans, which are thematized as “(im)permissible searching” and “(un)smooth posting,” on Chinese gay men. This study conceptualizes such algorithmic shadowbans as “algorithmic camouflage,” emphasizing the opacity of the roles, behaviors, and purposes of algorithms toward specific users from an […]
- by Kai-Hsin HungBig Data & Society, Volume 11, Issue 4, October-December 2024. We present a framework for viewing artificial intelligence (AI) as planetary assemblages of coloniality that reproduce dependencies in how it co-constitutes and structures a tiered global data economy. We use assemblage thinking to map the coloniality of power to demonstrate how AI stratifies across knowledge, geographies, and bodies to influence development and economic trajectories, impact workers, reframe domestic industrial policies, and reconfigure the international political economy. Our post-colonial framework unpacks AI through its (1) global, (2) meso, and (3) local layers, and further dissects how these layers are vertically integrated, […]
- by Stefania MilanBig Data & Society, Volume 11, Issue 4, October-December 2024. This essay argues that Latin American scholarship and movement practice are key to understanding the dynamics of the datafied society and countering its inequities. Examining the sources of inspiration of a frontrunner seeking to decolonize the datafied society – the Big Data from the South Initiative (BigDataSur) – we review Martín-Barbero's ontological shift from media to mediations, Freire's methodology centring individual agency and empowerment as a structural task of society, Mignolo's invite to take decoloniality as a praxis rather than merely an idea, Rodríguez's first-hand engagement with technology at the […]
- by Tomohiro IokuBig Data & Society, Volume 11, Issue 4, October-December 2024. As artificial intelligence (AI) becomes more integrated into society, concerns have arisen about unintended biases in AI-driven decision-making and the environmental impact of AI technology development. AI assistants such as Siri and Alexa, while helpful, can obscure decision-making and contribute to increased energy use and CO2 emissions. The present study explores whether consumers prioritize transparency and environmental sustainability over performance when choosing AI assistants with conjoint designs. Japanese participants were presented with different AI assistant profiles, varying in performance quality, transparency, cost, and environmental efficiency. The results revealed that Japanese […]
- by Daniel AshtonBig Data & Society, Volume 11, Issue 4, October-December 2024. This article investigates local government cultural economy data practices in England (UK). Engaging with academic literature and policy reports, it highlights the following issues relating to these data practices: types of data; the context and drivers for using data; the possibilities and challenges of data volume; and training and expertise required to position data in relation to strategic decision-making. Engaging with the authors’ research project with local government councillors and data officers working in local authorities in England, this article provides insights into situated experiences and working contexts. The analysis […]
- by Ilpo HelénBig Data & Society, Volume 11, Issue 4, October-December 2024. Our paper is a case study of the making of data-driven healthcare and anticipation work done by developer-experts in a project for implementation of an integrated patient data management platform in Finland. We focus on ‘personalised treatment plan’, a trope that experts regularly use when talking about the objectives of data management reform and their wishes for datafication of healthcare. We conceive of the personalised plan not primarily as a future vision or an outcome, but rather a tool of anticipation of work. Our analysis demonstrates two purposes for which […]
- by Michaela PaddenBig Data & Society, Volume 11, Issue 4, October-December 2024. This article discusses the perspectives of European Union (EU) / European Economic Area Data Protection Authorities (DPAs) on their role in protecting democratic rights and freedoms in digitalised societies. Data Protection Authorities, which are independent regulators, are responsible for implementing the EU's General Data Protection Regulation in their respective countries. The views of DPAs are important given their special role in monitoring newly emerging digital technologies and how their use may impact on the functioning of democracies. The article highlights three key themes which emerged in interviews with 18 DPAs […]
- by Adam John AndreottaBig Data & Society, Volume 11, Issue 4, October-December 2024. Online privacy policies or terms and conditions ideally provide users with information about how their personal data are being used. The reality is that very few users read them: they are long, often hard to understand, and ubiquitous. The average internet user cannot realistically read and understand all aspects that apply to them and thus give informed consent to the companies who use their personal data. In this article, we provide a basic overview of a solution to the problem. We suggest that software could allow users to delegate the […]
- by Skyler WangBig Data & Society, Volume 11, Issue 4, October-December 2024. Large language models (LLMs) and dialogue agents represent a significant shift in artificial intelligence (AI) research, particularly with the recent release of the GPT family of models. ChatGPT's generative capabilities and versatility across technical and creative domains led to its widespread adoption, marking a departure from more limited deployments of previous AI systems. While society grapples with the emerging cultural impacts of this new societal-scale technology, critiques of ChatGPT's impact within machine learning research communities have coalesced around its performance or other conventional safety evaluations relating to bias, toxicity, and […]
- by Kathryne MetcalfBig Data & Society, Volume 11, Issue 4, October-December 2024. Recent reporting has revealed that the UK Biobank (UKB)—a large, publicly-funded research database containing highly-sensitive health records of over half a million participants—has shared its data with private insurance companies seeking to develop actuarial AI systems for analyzing risk and predicting health. While news reports have characterized this as a significant breach of public trust, the UKB contends that insurance research is “in the public interest,” and that all research participants are adequately protected from the possibility of insurance discrimination via data de-identification. Here, we contest both of these claims. […]
- by Lisa Ann RicheyBig Data & Society, Volume 11, Issue 4, October-December 2024. Vexing political questions of power, inequality and coloniality permeate the tech sector and its growing use of global ‘virtual’ assembly lines that see them penetrate even refugee camps in efforts to extract value. As a response, tech companies have been expanding non-commercial activities within a presumed framework of humanitarianism, in part, trying to outweigh the negative implications of unjust business practices often characterised by third-party avoidance of responsibility. This commentary focuses on tech companies’ engagement with people in the Global South – not as recipients of tech beneficence – but […]