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| 2. |
- Birch, Kean, et al.
(författare)
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Data as asset? : The measurement, governance, and valuation of digital personal data by Big Tech
- 2021
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Ingår i: Big Data and Society. - : Sage Publications. - 2053-9517. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- Digital personal data is increasingly framed as the basis of contemporary economies, representing an important new asset class. Control over these data assets seems to explain the emergence and dominance of so-called “Big Tech” firms, consisting of Apple, Microsoft, Amazon, Google/Alphabet, and Facebook. These US-based firms are some of the largest in the world by market capitalization, a position that they retain despite growing policy and public condemnation—or “techlash”—of their market power based on their monopolistic control of personal data. We analyse the transformation of personal data into an asset in order to explore how personal data is accounted for, governed, and valued by Big Tech firms and other political-economic actors (e.g., investors). However, our findings show that Big Tech firms turn “users” and “user engagement” into assets through the performative measurement, governance, and valuation of user metrics (e.g., user numbers, user engagement), rather than extending ownership and control rights over personal data per se. We conceptualize this strategy as a form of “techcraft” to center attention on the means and mechanisms that Big Tech firms deploy to make users and user data measurable and legible as future revenue streams.
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| 3. |
- Bolin, Göran, 1959-, et al.
(författare)
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Heuristics of the Algorithm. Big Data, User Interpretation and Translation Strategies
- 2015
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Ingår i: Big Data and Society. - : SAGE Publications. - 2053-9517. ; 2:2, s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- Intelligence on mass media audiences was founded on representative statistical samples, analysed by statisticians at the market departments of media corporations. The techniques for aggregating user data in the age of pervasive and ubiquitous personal media (e.g. laptops, smartphones, credit cards/swipe cards and radio-frequency identification) build on large aggregates of information (Big Data) analysed by algorithms that transform data into commodities. While the former technologies were built on socio-economic variables such as age, gender, ethnicity, education, media preferences (i.e. categories recognisable to media users and industry representatives alike), Big Data technologies register consumer choice, geographical position, web movement, and behavioural information in technologically complex ways that for most lay people are too abstract to appreciate the full consequences of. The data mined for pattern recognition privileges relational rather than demographic qualities. We argue that the agency of interpretation at the bottom of market decisions within media companies nevertheless introduces a ‘heuristics of the algorithm’, where the data inevitably becomes translated into social categories. In the paper we argue that although the promise of algorithmically generated data is often implemented in automated systems where human agency gets increasingly distanced from the data collected (it is our technological gadgets that are being surveyed, rather than us as social beings), one can observe a felt need among media users and among industry actors to ‘translate back’ the algorithmically produced relational statistics into ‘traditional’ social parameters. The tenacious social structures within the advertising industries work against the techno-economically driven tendencies within the Big Data economy.
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| 4. |
- Bursell, Moa, et al.
(författare)
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After the algorithms : A study of meta-algorithmic judgments and diversity in the hiring process at a large multisite company
- 2024
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Ingår i: Big Data and Society. - : SAGE PUBLICATIONS INC. - 2053-9517. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, both private and public organizations across contexts have begun implementing AI technologies in their recruitment processes. This transition is typically justified by improved efficiency as well as more objective, performance-based ranking, and inclusive selection of job candidates. However, this rapid development has also raised concerns that the use of these emerging technologies will instead increase discrimination or enhance the already existing inequality. In the present study, we first develop the concept of meta-algorithmic judgment to understand how recruiting managers may respond to automation of the hiring process. Second, we draw on this concept in the empirical assessment of the actual consequences of this type of transition by drawing on two large and unique datasets on employment records and job applications from one of Sweden's largest food retail companies. By comparing the outcomes of traditional and algorithmic job recruitment during this technological transition, we find that, contrary to the company's intentions, algorithmic recruitment decreases diversity. However, in contrast to what is often assumed, this is primarily not because the algorithms are biased, but because of what we identify as an unintended human–algorithmic interaction effect.
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| 5. |
- Bursell, Moa, et al.
(författare)
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After the algorithms : A study of meta-algorithmic judgments and diversity in the hiring process at a large multisite company
- 2024
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Ingår i: Big Data and Society. - : SAGE PUBLICATIONS INC. - 2053-9517. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, both private and public organizations across contexts have begun implementing AI technologies in their recruitment processes. This transition is typically justified by improved efficiency as well as more objective, performance-based ranking, and inclusive selection of job candidates. However, this rapid development has also raised concerns that the use of these emerging technologies will instead increase discrimination or enhance the already existing inequality. In the present study, we first develop the concept of meta-algorithmic judgment to understand how recruiting managers may respond to automation of the hiring process. Second, we draw on this concept in the empirical assessment of the actual consequences of this type of transition by drawing on two large and unique datasets on employment records and job applications from one of Sweden's largest food retail companies. By comparing the outcomes of traditional and algorithmic job recruitment during this technological transition, we find that, contrary to the company's intentions, algorithmic recruitment decreases diversity. However, in contrast to what is often assumed, this is primarily not because the algorithms are biased, but because of what we identify as an unintended human-algorithmic interaction effect.
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| 6. |
- Engdahl, Isak
(författare)
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Agreements ‘in the wild’ : Standards and alignment in machine learning benchmark dataset construction
- 2024
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Ingår i: Big Data and Society. - 2053-9517. ; 11:2
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Tidskriftsartikel (refereegranskat)abstract
- This article presents an ethnographic case study of a corporate-academic group constructing a benchmark dataset of daily activities for a variety of machine learning and computer vision tasks. Using a socio-technical perspective, the article conceptualizes the dataset as a knowledge object that is stabilized by both practical standards (for daily activities, datafication, annotation and benchmarks) and alignment work – that is, efforts including forging agreements to make these standards effective in practice. By attending to alignment work, the article highlights the informal, communicative and supportive efforts that underlie the success of standards and the smoothing of tensions between actors and factors. Emphasizing these efforts constitutes a contribution in several ways. This article's ethnographic mode of analysis challenges and supplements quantitative metrics on datasets. It advances the field of dataset analysis by offering a detailed empirical examination of the development of a new benchmark dataset as a collective accomplishment. By showing the importance of alignment efforts and their close ties to standards and their limitations, it adds to our understanding of how machine learning datasets are built. And, most importantly, it calls into question a key characterization of the dataset: that it captures unscripted activities occurring naturally ‘in the wild’, as alignment work bleeds into moments of data capture.
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| 7. |
- Enlund, Desirée, PhD, 1984-, et al.
(författare)
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The role of sensors in the production of smart city spaces
- 2022
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Ingår i: Big Data and Society. - : SAGE Publications Ltd. - 2053-9517. ; 9:2
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 8. |
- Flyverbom, Mikkel, et al.
(författare)
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Datastructuring—Organizing and curating digital traces into action
- 2018
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Ingår i: Big Data and Society. - : SAGE Publications. - 2053-9517. ; 5:2
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Tidskriftsartikel (refereegranskat)abstract
- Digital transformations and processes of datafication fundamentally reshape how information is produced, circulated and given meaning. In this article, we provide a concept of datastructuring which seeks to capture this reshaping as both a product of and productive of social activity. To do this we focus on (1) how new forms of social action map onto and are enabled by technological changes related to datafication, and (2) how new forms of datafied social action constitute a form of knowledge production which becomes embedded in technologies themselves. We illustrate the potential of the datastructuring concept with empirical examples which also serve to highlight some new avenues for research and some empirical questions to explore further. We suggest a focus on datastructuring can ignite scholarly debates across disciplines that may share an interest in the technological configurations, sorting activities, and other socio-material forces that shape digital spaces, but which are rarely brought together. Such cross-disciplinary conceptualizations may give more attention to how information is structured and organized, becomes algorithmically recognizable, and emerges as (in)visible in digital, datafied spaces. Such a concept, we suggest, may help us better understand the novel ways in which backstage datawork and data sorting processes gain traction in political interventions, commercial processes, and social ordering.
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| 9. |
- Haider, Jutta, et al.
(författare)
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Google Search and the creation of ignorance: The case of the climate crisis
- 2023
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Ingår i: Big Data and Society. - : SAGE Publications. - 2053-9517. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- The article examines the relationship between commercial search engines, using Google Search as an example, and various forms of ignorance related to climate change. It draws on concepts from the field of agnotology to explore how environmental ignorances, and specifically related to the climate crisis, are shaped at the intersection of the logics of Google Search, everyday life and civil society/politics. Ignorance refers to a multi-facetted understanding of the culturally contingent ways in which something may not be known. Two research questions are addressed: How are environmental ignorances, and in particular related to the climate crisis, shaped at the intersection of the logics of Google Search, everyday life and civil society/politics? In what ways can we conceptualise Google's role as configured into the creation of ignorances? The argument is made through four vignettes, each of which explores and illustrates how Google Search is configured into a different kind of socially produced ignorance: (1) Ignorance through information avoidance: climate anxiety; (2) Ignorance through selective choice: gaming search terms; (3) Ignorance by design: algorithmically embodied emissions; (4) Ignorance through query suggestions: directing people to data voids. The article shows that while Google Search and its underlying algorithmic and commercial logic pre-figure these ignorances, they are also co-created and co-maintained by content producers, users and other human and non-human actors, as Google Search has become integral of social practices and ideas about them. The conclusion draws attention to a new logic of ignorance that is emerging in conjunction with a new knowledge logic.
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| 10. |
- Harron, Katie, et al.
(författare)
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Challenges in administrative data linkage for research
- 2017
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Ingår i: Big Data and Society. - : SAGE Publications. - 2053-9517. ; 4:2
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Tidskriftsartikel (refereegranskat)abstract
- Linkage of population-based administrative data is a valuable tool for combining detailed individual-level information from different sources for research. While not a substitute for classical studies based on primary data collection, analyses of linked administrative data can answer questions that require large sample sizes or detailed data on hard-to-reach populations, and generate evidence with a high level of external validity and applicability for policy making. There are unique challenges in the appropriate research use of linked administrative data, for example with respect to bias from linkage errors where records cannot be linked or are linked together incorrectly. For confidentiality and other reasons, the separation of data linkage processes and analysis of linked data is generally regarded as best practice. However, the black box' of data linkage can make it difficult for researchers to judge the reliability of the resulting linked data for their required purposes. This article aims to provide an overview of challenges in linking administrative data for research. We aim to increase understanding of the implications of (i) the data linkage environment and privacy preservation; (ii) the linkage process itself (including data preparation, and deterministic and probabilistic linkage methods) and (iii) linkage quality and potential bias in linked data. We draw on examples from a number of countries to illustrate a range of approaches for data linkage in different contexts.
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