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"Tubaro, Paola"
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Disembedded or Deeply Embedded? A Multi-Level Network Analysis of Online Labour Platforms
This article extends the economic-sociological concept of embeddedness to encompass not only social networks of, for example, friendship or kinship ties, but also economic networks of ownership and control relationships. Applying these ideas to the case of digital platform labour pinpoints two possible scenarios. When platforms take the role of market intermediaries, economic ties are thin and workers are left to their own devices, in a form of ‘disembeddedness’. When platforms partake in intricate inter-firm outsourcing structures, economic ties envelop workers in a ‘deep embeddedness’ which involves both stronger constraints and higher rewards. With this added dimension, the notion of embeddedness becomes a compelling tool to describe the social structures that frame economic action, including the power imbalances that characterize digital labour in the global economy.
Journal Article
The trainer, the verifier, the imitator: Three ways in which human platform workers support artificial intelligence
This paper sheds light on the role of digital platform labour in the development of today’s artificial intelligence, predicated on data-intensive machine learning algorithms. Focus is on the specific ways in which outsourcing of data tasks to myriad ‘micro-workers’, recruited and managed through specialized platforms, powers virtual assistants, self-driving vehicles and connected objects. Using qualitative data from multiple sources, we show that micro-work performs a variety of functions, between three poles that we label, respectively, ‘artificial intelligence preparation’, ‘artificial intelligence verification’ and ‘artificial intelligence impersonation’. Because of the wide scope of application of micro-work, it is a structural component of contemporary artificial intelligence production processes – not an ephemeral form of support that may vanish once the technology reaches maturity stage. Through the lens of micro-work, we prefigure the policy implications of a future in which data technologies do not replace human workforce but imply its marginalization and precariousness.
Journal Article
A meso-scale cartography of the AI ecosystem
Recently, the set of knowledge referred to as “artificial intelligence” (AI) has become a mainstay of scientific research. AI techniques have not only greatly developed within their native areas of development but have also spread in terms of their application to multiple areas of science and technology. We conduct a large-scale analysis of AI in science. The first question we address is the composition of what is commonly labeled AI, and how the various subfields within this domain are linked together. We reconstruct the internal structure of the AI ecosystem through the co-occurrence of AI terms in publications, and we distinguish between 15 different specialties of AI. Furthermore, we investigate the spreading of AI outside its native disciplines. We bring to light the dynamics of the diffusion of AI in the scientific ecosystem and we describe the disciplinary landscape of AI applications. Finally we analyze the role of collaborations for the interdisciplinary spreading of AI. Although the study of science frequently emphasizes the openness of scientific communities, we show that collaborations between those scholars who primarily develop AI and those who apply it are quite rare. Only a small group of researchers can gradually establish bridges between these communities.
Journal Article
Epistemic integration and social segregation of AI in neuroscience
In recent years, Artificial Intelligence (AI) shows a spectacular ability of insertion inside a variety of disciplines which use it for scientific advancements and which sometimes improve it for their conceptual and methodological needs. According to the transverse science framework originally conceived by Shinn and Joerges, AI can be seen as an instrument which is progressively acquiring a universal character through its diffusion across science. In this paper we address empirically one aspect of this diffusion, namely the penetration of AI into a specific field of research. Taking neuroscience as a case study, we conduct a scientometric analysis of the development of AI in this field. We especially study the temporal egocentric citation network around the articles included in this literature, their represented journals and their authors linked together by a temporal collaboration network. We find that AI is driving the constitution of a particular disciplinary ecosystem in neuroscience which is distinct from other subfields, and which is gathering atypical scientific profiles who are coming from neuroscience or outside it. Moreover we observe that this AI community in neuroscience is socially confined in a specific subspace of the neuroscience collaboration network, which also publishes in a small set of dedicated journals that are mostly active in AI research. According to these results, the diffusion of AI in a discipline such as neuroscience didn’t really challenge its disciplinary orientations but rather induced the constitution of a dedicated socio-cognitive environment inside this field.
Journal Article
Counting ‘micro-workers’: societal and methodological challenges around new forms of labour
‘Micro-work’ consists of fragmented data tasks that myriad providers execute on online platforms. While crucial to the development of data-based technologies, this poorly visible and geographically spread activity is particularly difficult to measure. To fill this gap, we combined qualitative and quantitative methods (online surveys, in-depth interviews, capture-recapture techniques and web traffic analytics) to count micro-workers in a single country, France. On the basis of this analysis, we estimate that approximately 260,000 people are registered with micro-work platforms. Of these, some 50,000 are ‘regular’ workers who do micro-tasks at least monthly and we speculate that using a more restrictive measure of ‘very active’ workers decreases this figure to 15,000. This analysis contributes to research on platform labour and the labour in the digital economy that lies behind artificial intelligence.
Journal Article
Learners in the loop: hidden human skills in machine intelligence
Today's artificial intelligence, largely based on data-intensive machine learning algorithms, relies heavily on the digital labour of invisibilized and precarized humans-in-the-loop who perform multiple functions of data preparation, verification of results, and even impersonation when algorithms fail. Using original quantitative and qualitative data, the present article shows that these workers are highly educated, engage significant (sometimes advanced) skills in their activity, and earnestly learn alongside machines. However, the loop is one in which human workers are at a disadvantage as they experience systematic misrecognition of the value of their competencies and of their contributions to technology, the economy, and ultimately society. This situation hinders negotiations with companies, shifts power away from workers, and challenges the traditional balancing role of the salary institution.
Paper
The role of digital platforms in market coordination through quality valuations. The case of restaurants
How do digital platforms affect coordination in the restaurant market? In particular, how do they reshape firms’ positions in the quality space and their dependence on both consumers’ valuations and competitors’ choices? Focusing on the case of a widely used platform for restaurant booking and reviewing, we analyze the dine-in services market in the city of Lille, France. In line with economic sociology’s definition of markets as concrete social spaces, we frame these restaurants as a producer market in which multiple quality conventions coexist. We use sequential mixed methods and data (observations and interviews, web-scraping and business data) to show that platforms rationalize firms’ practice of observing one another as a basis for making decisions on volume and quality. The rise of digital platforms provides producers with devices that amplify their view of competitors, standardize their offerings and support the stability of their business choices over time, conditional on spatial constraints and quality choices.
Journal Article
Sociology and Sociology Networks
A review essay covering books by: 1) Christina Prell, Social Network Analysis: History, Theory and Methodology (2011); 2) Thomas W Valente, Social Networks and Health: Models, Methods and Applications (2010); and 3) Matthew O Jackson, Social and Economic Networks (2010).
Journal Article