Digital labour – Data Big and Small

American AI, made in Venezuela

The political tensions between Venezuela and the United States are at an all-time high, after news of strikes on Caracas and the alleged capture of President Maduro and his wife. The recent escalation follows years of economic sanctions and a deep divide between the two countries.

And yet, Venezuela has massively supplied cheap data work to US-based technology producers throughout all these years. Through digital labour platforms, an educated but impoverished workforce made its way to (the bottom of) the supply chains of US-directed artificial intelligence (AI).

Since about 2017, high inflation, increasing scarcity of even basic goods, and widespread poverty have pushed Venezuelans to work for international platforms that pay in US dollars, albeit at low rates. They have come to constitute a large reservoir for technology producers, mainly (though not only) in the United States. Known for their willingness to accept even the lowest pay rates in the data work market, Venezuelan workers have annotated hundreds of thousands of videos and images for the development of (for example) self-driving vehicles. Ironically, the very policies of Chavist governments – from Chávez himself to Maduro – made this possible. Cheap access to electricity and promotion of digital literacy, including through the widespread distribution of locally produced computers (‘Canaima’) to students and schoolchildren, provided people with the necessary infrastructure to perform data work. Even outdated and malfunctioning, these equipments played a crucial role in enabling widespread Venezuelan participation to the AI pipeline.

Nicolas Gourault (2020). VO: A documentary and sensory investigation about the role of human workers in the training of driverless cars. Source: https://nicolasgourault.fr/films/vo

For Venezuelan workers, platforms labour has constituted a a resilience strategy against adverse local conditions. Participation has never been easy owing to frequent power cuts, slow internet connection, and aging devices, not to mention the difficulty of working almost entirely in English. The high educational levels and computing skills of many workers (including experienced professionals and science/technology students), and embeddedness in densely knit networks of support offered solutions. At the same time, work on platforms is not without challenges, and all Venezuelan data workers have experienced some form of disrespect. Being paid less than peers in neighbouring countries, or even being offered fewer tasks than these foreign peers, are examples of this. At some point, they had to endure a widespread perception that they do not work well. Resisting against international platforms can be more challenging than bypassing local restrictions, and Venezuelan workers limited themselves to occasional acts of minor cheating involving only very few of them.

Venezuelans’ resolve to move out of the crisis and the networked relationships that sustain each of them against hardship have made them massively present, as ‘uninvited protagonists,’ in international data work platforms. Conversely, some AI companies and platforms (from the Global North in general, and from the United States specifically) targeted Venezuela deliberately, not much for the qualities and skills of its highly educated population, but for its low cost at a time of crisis. These platform-mediated encounters enabled short-term solutions, but haven’t raised Venezuela out of poverty, and haven’t ensured a durable provision of high-quality data for AI.

What comes next inside Venezuela is deeply unclear, but unfortunately, nothing (for now) suggests any recognition of the role of these workers in the technology industry, or any opportunity to reshape its outputs in more equitable and respectful ways.

The dual footprint of AI

The impacts of artificial intelligence (AI) on the natural and social surroundings that supply resources for its production and use have been studied separately so far. In a new article, part of a forthcoming special issue of the journal Globalizations, I introduce the concept of the ‘dual footprint’ as a heuristic device to capture the commonalities and interdependencies between them. Originally borrowed from ecology, the concept denotes in my analysis the total impacts on the natural and social surroundings that supply the resources necessary for AI’s production and use. It is an indicator of sustainability insofar as it grasps the degree to which the AI industry is failing to ensure the maintenance of the social systems, economic structures, and environmental conditions necessary to its production. To develop the concept in this way, it is necessary to (provisionally) renounce some of the accounting flavour of extant footprint measures, allowing for a more descriptive interpretation. In my article, the dual footprint primarily serves as a mapping tool, linking impacts to specific locations and to the people and groups that inhabit them.

Gloria Mendoza / ‘The Environmental Impact of Data Centers in Vulnerable Ecosystems’ / https://betterimagesofai.org / https://creativecommons.org/licenses/by/4.0/

My analysis draws on recent research that challenges idealized narratives of AI as the sole result of mathematics and code, or as the fancied machinic replacement of human brains. The production of AI relies on global value chains which, like those of textiles and electronics, take shape within the broader context of globalization, its long-standing trends of outsourcing and offshoring, and the cross-country disparities on which it thrives.

The argument is based on two case studies, each illustrating AI-induced cross-country flows of natural resources and data labour. The first involves Argentina as a supplier to the United States, while the second includes Madagascar and its primary export destinations: Japan and South Korea for raw materials, France for data work. These two cases portray the AI landscape as an asymmetric structure, where the countries that lead the tech race generate a massive demand for imports of raw materials, components, and intermediate goods and services. Core AI producers trigger the footprint and therefore should bear responsibility for it, but the pressure on (natural and social) resources and the ensuing impacts occur predominantly elsewhere. Cross-country value chains shift the burden toward more peripheral players, obscuring the extent to which AI is material- and labour-intensive.

Flows of raw materials (mainly nickel and cobalt from the Ambatovy mining project) from Madagascar to East Asia and, to a lesser extent, Europe and North America (top); flows of data work services from Madagascar to France, followed by North America and to a lesser extent, East Easia (bottom). Madagascar, one of the poorest countries in the world, contributes to state-of-the-art AI production without managing to move up the value chain.

This drain of resources toward AI engenders adverse effects in peripheral countries. Mining notoriously generates conflicts, and data work conditions are so poor that other segments of society – from local employers to workers’ families and even informal-economy actors – must step in to cover part of the costs. The current arrangements thus fail to ensure their own sustainability over time. Additionally, the aspirations of these countries to leverage their participation to the AI value chain as a development opportunity, and to transition toward leading positions, remain unfulfilled.

The dual footprint can fruitfully dialogue with the critical literature that leverages the concepts of extractivism (for example, Cecilia Rikap‘s concept of “twin” extractivism) and dependency (as theorised for example by Jonas Valente and Rafael Grohmann). Its contribution lies mainly in the effort to operationalise the ideas of more abstract social theories, while also facilitating mutual enrichment between different literatures.

Read the full paper: subscription-protected or open-access preprint.

The paper was developed as part of an initiative on ‘The Political Economy of Green-Digital Transition‘, organised by Edemilson Paraná in 2024 at LUT University in Finland. Further, the idea that the environmental and social dimensions of AI production emanate from similar underlying socio-economic processes and geographical trajectories constitutes the foundation of SEED – Social and Environmental Effects of Data Connectivity, a new DiPLab project that investigates how data extraction and material extraction are deeply interconnected. It stems from a collaboration with Núcleo Milenio FAIR at the Pontificia Universidad Católica de Chile and compares data and material infrastructures in Europe and South America.

Digital labor in the Middle East and Africa: Emerging trends, challenges, and opportunities

Following the success of the inaugural INDL-MEA Conference in 2024, the second event of the Middle East and Africa chapter of the International Network on Digital Labor (INDL-MEA-2) will take place exclusively online on 25-26 November 2025. The conference will serve as a key regional forum for researchers, policymakers, and practitioners engaged in studying and shaping the future of digital labor, gig work, data work, content moderation, and technology-related jobs in the Middle East and Africa.

Digital labor continues to evolve as a defining feature of global and regional economies, shaping employment opportunities, economic structures, and policy debates. The Middle East and Africa present unique dynamics in digital labor, characterized by platformization, algorithmic management, labor informality, and digital entrepreneurship, alongside issues of regulation, fair work practices, and digital workers’ agency.

With INDL-MEA’s second edition, we aim to enhance interdisciplinary and policy-relevant insights into platform work, automation, labor protections, and digital rights in the region. The programme is available here, and it is still possible to register here.

Sociology of AI, Sociology with AI (1)

There are two main ways in which a discipline like sociology engages with artificial intelligence (AI) and is affected by it. The sociology of AI understands technology as embedded in socio-economic systems and takes it as an object for research. Sociology with AI indicates that the discipline is also integrating AI into its methodological toolbox. Based on a talk that I gave at this year’s annual meeting of the European Academy of Sociology, I’ll give in what follows a brief overview of both. As a disclaimer, I have no pretention to be exhaustive. To narrow down the topic, I have chosen to focus on sociology specifically (rather than neighboring fields), and to rely only on already published, peer-reviewed research.

Anne Fehres and Luke Conroy & AI4Media, “Data is a Mirror of Us”/ https://betterimagesofai.org / https://creativecommons.org/licenses/by/4.0/

Let’s start with the sociology of AI, which I’ll illustrate with the help of the above artwork. Its aim is to demonstrate that even if there is a sense of magic in looking at the outputs of an AI system, the data on which it is based has a human origin. This work explores this idea through the symbolism of the mirror and reflection: beyond the magic, these outputs are a reflection of society. Sociological perspectives matter because they can help bring these social and human origins to the fore. In 2021, Kelly Joyce and her coauthors called for more engagement of sociologists in outlining a research agenda around these topics. Compared to other disciplines, we have a thicker understanding of the intersectional inequalities and social structures that interact with AI.

However, it was not sociology that initiated the conversation on these issues. Disciplines like computer science itself, communication, philosophy, and the arts shaped the debate. Landmark contributions were, among other things, a 2016 influential journalistic report about discrimination in predictive police applications, a 2018 computer science article on gender and race discrimination in face recognition, and an artistic project which, also in 2018, described Amazon Echo as an anatomical map of human labor, data and planetary resources. Conferences like ACM’s FaccT have become reference venues for these analyses. For clarity, some of the contributors to these debates are indeed sociologists but the discipline’s infrastructure of conferences, journals and institutions, has been less responsive.

Why does the quasi-absence of sociology matter? I’ll answer this question through a 2022 paper, written by two sociologists but published in a computer science conference. The starting point is that early studies framed AI-related societal problems in terms of bias. For example, the above-mentioned report on predictive policing was entitled “machine bias”. This language points to technical corrections as remedy, but it cannot account for the social processes underway that comprise, among other things, increasing surveillance and privacy intrusion to collect more and more data (see image below). De-biasing may thus be insufficient to prevent injustice or inequality. A sociologically informed approach reveals that key questions are about power: who owns data and systems, whose worldviews are being imposed, whose biases we are trying to mitigate.

Comuzi/ ‘’SurveillanceView’’ / https://betterimagesofai.org / © BBC / https://creativecommons.org/licenses/by/4.0/

In recent years, more substantial contributions have been made within sociology. For example, there was a special issue of Socius last year on “Sociology of Artificial Intelligence”, and another one is forthcoming in Social Science Computer Review, entitled “What is Sociological About AI?”. I’ll mention a non-exhaustive selection of topics and findings. First, sociologists have recognized the hype – or how financial, political, and other interests have boosted the circulation of (often) exaggerated claims. This means shifting the gaze from AI as an intellectual endeavor, to see AI as a market – where bubbles can, well, form. This also means recognizing the political dimensions of AI development, with many states using public funding as a crucial engine for innovation.

Second, AI practitioners engage in a form of social construction of morality to legitimate their approaches to AI. For example, some distance themselves from Big Tech capitalism, some insist on the benefits of some AI applications, most prominently in healthcare. These efforts ultimately shape which technologies gain visibility and attract capital investments. This is also a way through which they produce and sustain the AI bubble itself – a culturally embedded market phenomenon. Third, sociological analysis can move beyond the technological determinism of early AI critics to emphasize the social and institutional contexts within which such algorithmic decision-making systems are deployed. This brings to light forms of negotiation, adaptation, and resistance, which have more subtle effects on inequalities.

Nacho Kamenov & Humans in the Loop : “Data annotators labeling data” / https://betterimagesofai.org / https://creativecommons.org/licenses/by/4.0/

Fourth, there is labor. Beyond fears of job losses due to AI, sociological research has unveiled a growing labor demand to produce AI itself. This does not only include the work of elite engineers and computer scientists, but also the lower-level contributions of data annotators, content moderators, voice actors, transcriptors, translators, image labelers, prompt testers, and even very basic clickworkers. This work is typically outsourced and offshored, resulting in precarious working arrangements and low pay. The above photograph represents two workers who use this job as a means of livelihoods. Overall, there is no drop in employment levels, but a steady deterioration of working conditions and an accelarated shift of the power balance from labor to capital. AI affects the very labor that produces it.

In sum, sociologists increasingly contribute to these conversations, although these topics are not prominent in the discipline’s flagship conferences and journals, and important knowledge gaps remain. The guest-editors of the forthcoming Social Science Computer Review special issue on “What is sociological about AI?” claim that “A sociological lens can render AI’s hidden processes legible, just as sociologists have done with complex and taken for granted social forces since the discipline’s inception”. They nevertheless note that “we neither have a robust concept of AI as a social phenomenon nor a holistic sociological discourse around it, despite vibrant and dynamic work in the area.” In passing, most extant studies rely on traditional methods, primarily surveys and fieldwork. This is not an issue in itself, but it highlights a disconnection with the sub-topic I’ll highlight in my next post – Sociology using AI as instrument.

A successful INDL-8 conference in Bologna

When we created ENDL (the European Network on Digital Labour), back in 2017, we booked a room with 17 places. A few days ago, the last conference of the network (which in the meantime has become INDL, replacing ‘European’ with ‘International’) hosted about 200 participants. Internationalisation has not only meant numerical growth, but also inclusion of a diverse range of voices: every year, we see more participants from countries that are often under-represented on the scientific scene, from India and South Africa to Argentina and Brazil. Participants have also diversified in another sense, too: if the majority have always been academics, it is a pleasure to see more and more workers, as well as labour organisers. This year, we could for example benefit from the presence of associations of data workers from Kenya, freelancers from France, and content moderators from Spain.

Participants to the INDL-8 conference, Saint-Cristina cloister, Bologna, IT, 10 September 2025.

A conference like this one is meant to give hope – hope of mutual understanding across countries and cultures, hope of dialogue across disciplines and fields, hope of connections between academic research and action. We worked together to ensure a welcoming environment for all, for instance by encouraging constructive comments, rather than sheer criticism, after each paper presentation. We also strived to keep costs down in order to make the conference free of charge, and with the DiPLab research programme, we could give a few small scholarships to promising presenters who might not have been able to travel otherwise.

Two speakers (M Francesco Sinopoli, Fondazione Di Vittorio, and Ms Kauna Malgwi, Uniglobal) at the plenary panel ‘Plenary panel: New Unionism, towards global alliances’, part of the INDL-8 Conference, DAMA Tecnopolo, Bologna, IT, 11 September 2025

Surely, problems remain. A couple potential participants had visa issues, while others had to cancel due to lack of funding. These problems weigh especially hard on people from emerging and lower-income countries outside Europe and North America. The future is also uncertain, as funding sources become increasingly dryer, and visa restrictions tighter. For this reason, the main INDL-9 conference next year (Geneva, ILO, 9-11 September 2026) will be accompanied by the growth of local chapters. The Middle-East and Africa area is preparing its second conference, this time online only, on 25-26 November. In the US, a one-day event will take place at Yale University on 29 April 2026. Colleagues in Chile and Argentina are launching a series of online events.

Closing keynote (Prof. Sandro Mezzadra, chair: Prof. Marco Marrone), Saint-Cristina Aula Magna, Bologna, IT, 12 September 2025

More information on the INDL-8 conference (including the full programme) is available here.

Women in the loop: the gendered contribution of data workers to AI

I presented today, at the WORK2025 conference in Turku, Finland, a paper on the human-in-the-loop systems that integrate human labor into the production of Artificial Intelligence (AI). Beyond engineers who design models, myriad “data workers” prepare training data, verify outputs, and correct errors. Their role is crucial but undervalued, with low pay and poor working conditions. Shaped by outsourcing and offshoring practices, the market for such services has grown steadily over time, with digital platforms acting as the main intermediaries between AI producers and workers. In their communication with clients, these platforms often emphasize that human workers provide nuanced judgment in complex tasks.

The three main functions of micro-work in the development of data-intensive, machine-learning based AI solutions. Source: https://doi.org/10.1177/2053951720919776

But who are the humans in the loop, and whose contributions count? Here, I focus on women’s participation and its evolution as the market expanded. Data work is theoretically well-suited for women, since it can be performed remotely from home. Besides, platforms generally do not share gender information, thereby limiting direct discrimination. One might thus expect women’s representation to be high. However, the statistical evidence is mixed. Across studies, the proportion of women data workers exceeds 50% only in four cases. Besides, reports sometimes differ for the same country, across platforms or at different moments in time. Looking at the lowest reported shares, then in no country except the US do women represent more than 40% of all data workers. Even in the US, recent data indicate that women constitute about half of the data workforce, down from 57-58% some years ago. Why are women underrepresented, and why does the pattern vary across countries?

Highest proportion of women data workers reported in existing studies (incl. own datasets). Source: author’s elaboration, created with MapChart.

Lowest proportion of women data workers reported in existing studies (incl. own datasets). Source: author’s elaboration, created with MapChart.

The earliest explanation comes from P. Ipeirotis (2010), who analyzed Amazon Mechanical Turk, then the dominant platform. Most workers were from the US and India. In the US, data work paid too little to sustain a household and was often taken up by un- and under-employed women seeking supplementary income. In India, dollar-based pay was more attractive and often a main household income, drawing more men into the activity. Later, as the market expanded, this explanation appeared insufficient: the above maps show that not all rich countries have many female data workers, and some lower-income countries do. Yet, my data show a negative correlation: the larger the share of workers for whom data work is the main income source, the smaller the proportion of women. Ipeirotis’s hypothesis still holds but requires updating to today’s more competitive and globalized platform economy.

Proportion of workers for whom data work is the main source of income vs. proportion of women, by country. Source: own survey data (from projects TRIA and ENCORE, 2020-24).

Platforms fragment work into tasks and assign them to individuals framed as independent contractors competing for access. Unlike traditional firms, workers do not collaborate but face intense competition. Outcomes vary by national context. In countries facing stagnation or crisis, such as Venezuela, international platforms offer a rare source of income for highly qualified workers. Competition becomes fierce, and “elite” workers – often young men with STEM backgrounds – dominate. Women are disadvantaged, either due to fewer technical qualifications or because care responsibilities limit their ability to invest in building strong platform profiles and reputations. By contrast, in more dynamic economies such as Brazil, local job markets absorb highly skilled professionals, leaving platform work to more disadvantaged groups. Here, women with family duties are more visible. Thus, platform demographics reflect national conditions: in poorer or crisis-stricken countries, men from the educational elite seek career advancement, while in richer countries, women (especially mothers) take on such work primarily to supplement household income. Women may be equally educated, but they often lack the time to cultivate advanced STEM skills. As platforms demand longer and more specialized tasks, men increasingly gain the upper hand, crowding women out—even in countries where they were once the majority.

Platform design ignores these dynamics. Workers are treated as abstract entities, stripped of the socio-economic and cultural contexts that shape real inequalities. Competition, combined with local conditions, deepens gender gaps. Interventions must therefore consider gender disparities. Otherwise, they risk reinforcing inequalities. Supporting women’s access to data work—particularly those constrained by family responsibilities—can contribute to more balanced labor participation and ensure that AI benefits from a broader diversity of human input.

AI, labour and natural resources in Santiago

Last week in Santiago, Chile, I had the tremendous opportunity to give a keynote speech at the 4th annual workshop of the Millennium Nucleus on the Evolution of Work (M-NEW), of which I am also a senior international member. This interdisciplinary workshop brought together labour scholars from various parts of Latin America and beyond. I really liked the inspiring talks and the friendly and stimulating interactions with colleagues.

My own talk drew on my multi-year research programme on the crucial yet invisible human labor behind the global production of artificial intelligence. I first examined the evolution of this form of work over the last two decades, demonstrating that while its core functions in the development of smart systems have remained consistent, the scope and volume of such tasks have expanded significantly. I then analyzed the organization of this labour at the intersection of three trends in recent globalization: outsourcing, offshoring, and digitalization. These dynamics account for the marginalization of these workers within the tech industry and the relocation of their labor to lower-wage countries. Based on these insights, I described four cases—Venezuela, Argentina, Brazil, and Chile—highlighting the diverse effects of local conditions. I concluded by identifying emerging scientific and policy challenges, particularly concerning the recognition of skills, and the place of the informal economy.

The following week, still in Santiago, I was excited to participate in the kick-off meeting of the new research project SEED (“Social and Environmental Effects of Data connectivity: Hybrid ecologies of transoceanic cables and data centers in Chile and France”), a collaboration between my research group DiPLab and another Millennium Nucleus, FAIR (“Futures of Artificial Intelligence Research”). SEED received joint funding from the ECOS-SUD programme (France) and ANID (Chile) to analyse the AI value chain, from its production and development to its impact on employment, use and environmental consequences, by studying the Valparaíso-Santiago de Chile and Marseille-Paris axes.

My presentation introduced the concept of the ‘dual footprint’ as a heuristic device to capture the commonalities and interdependencies between the different impacts of AI on the natural and social surroundings that supply resources for its production and use. I framed the AI industry as a value chain that spans national boundaries and perpetuates inherited global inequalities. The countries that drive AI development generate a massive demand for inputs and trigger social costs that, through the value chain, largely fall on more peripheral actors. The arrangements in place distribute the costs and benefits of AI unequally, resulting in unsustainable practices and preventing the upward mobility of more disadvantaged countries. The dual footprint grasps how the environmental and social dimensions of AI emanate from similar underlying socio-economic processes and geographical trajectories.

Desinformación, trabajo y plataformas en Buenos Aires

Acabo de regresar de un viaje muy lindo a Argentina, donde fui invitada por el Instituto francés para participar en varios eventos.

El 12 de mayo, participé en la conferencia “Manipulación Informativa e Injerencia Extranjera: Desafíos Globales y Respuestas Democráticas”, organizada por la Delegación de la Unión Europea en Argentina y por varias embajadas (como la de Francia). En el panel “Cómo contrarrestar la desinformación respetando la libertad de expresión y el derecho a la información”, hablé de cómo la desinformación se financia a través del mercado publicitario que sustenta toda la web, y de la necesidad de regular este mercado. También destaqué la importancia de fortalecer la investigación científica sobre este tema – como en el proyecto AI4TRUST, financiado por la propia UE.

Al día siguiente, tuve dos encuentros con estudiantes de periodismo, en la Universidad Nacional de Avellaneda y en la Universidad Abierta Interamericana, también sobre temas relacionados con la desinformación en internet. Fue un honor y un placer ver, en cada caso, la sala llena y mucho interés. Los/as estudiantes hicieron muchas preguntas y demostraron una gran disposición a aprender y progresar.

Los días 16 y 17 de mayo participé en tres paneles organizados en el marco de la Noche de las Ideas, una iniciativa del Instituto francés que se organiza todos los años y esta vez tuvo lugar en el famoso Teatro Colón de Buenos Aires. Fui en la sesión inaugural sobre el tema de este año, “El poder de actuar”. Además, participé como ponente en un debate muy interesante sobre el trabajo en plataformas digitales que se titulaba provocativamente ¿Nuevas servidumbres voluntarias? Jóvenes y precariedad y en otro sobre la inteligencia artificial: “In A.I. we trust?”. Actuar con y en contra de las nuevas tecnologías.

El 20 de mayo, di una charla sobre “El futuro del trabajo y la IA” como parte del ciclo UBA Digital, en la Universidad de Buenos Aires. Presenté algunos resultados de mi investigación sobre el trabajo digital y su papel en la producción de IA, desarrollada en el marco del programa de investigación DiPLab. Una vez más, me alegró ver a muchos participantes con preguntas muy interesantes. Fuimos acogidos por la facultad de odontología y también tuvimos la extraordinaria oportunidad de visitar la clínica.

Uninvited protagonists: the social networks of Venezuelan AI data workers

After years of work, the long-awaited good news: my article ‘Uninvited Protagonists: The Networked Agency of Venezuelan Platform Data Workers‘, co-authored with Juana Torres-Cierpe, has just been published in New Technology, Work and Employment!

Workers in Venezuela are powering AI production, often under tough conditions. Sanctions and a deep political-economic crisis have pushed them to work for platforms that pay in US dollars, albeit at low rates. They constitute a large reservoir for technology producers from rich countries. But they are not passive players.

They build resilience, rework their environment, and sometimes engage in acts of resistance, with support from different segments of their personal networks. From strong local ties to loose online connections, these informal webs help them cope, adapt, and occasionally push back. Their diversified relationships comprise an unofficial and often hidden, albeit largely digitised relational infrastructure that sustains their work and shapes collective action.

These findings invite to rethink agency as embedded in workers’ personal networks. To respond to adversities, one must liaise with equally affected peers, with family and friends who offer support, etc. Social ties ultimately determine who is enabled to respond, and who is not; whether any benefits and costs are shared, and with whom; whether any solution will be conflictual or peaceful. Social networks are not accessory but constitute the very channel through which Venezuelan data workers cope with hardship.

Not all relationships play the same role, though. Venezuelans discover online data work through their strong ties with family, close friends, and neighbours. To convert their online earnings into local currency, they rely on their broader social networks of relatives and friends living abroad and indirect relationships with intermediaries. For managing their day-to-day activities, Venezuelans expand their social networks through online services like Facebook, WhatsApp, and Telegram, connecting with diverse and less-close peers within and outside the country. Different social ties affect the various stages of the data working experience.

Overall, no Venezuelan could work alone – and the networked interactions that sustain each of them against hardship have made them massively present, as ‘uninvited protagonists,’ in international platforms. Their massive presence in the planetary data-tasking market is a supply rather than demand-driven phenomenon.

This analysis also sheds light on the reasons why mobilisation is uncommon among platform data workers. Other studies noted diverging orientations of workers, unclear goals, lack of focus, and insufficient leadership. Another powerful reason hinges upon the predominance of weak ties in building up online group membership: indeed, distant acquaintances are insufficient to prompt people to action if their intrinsic motivations are low.

The article is available in open access here.

Call for Abstracts: INDL-8 Conference in Bologna

It is my pleasure to announce that the call for abstracts for the upcoming INDL-8 conference is now open.

The conference reaches Italy this year. It will take place in the most ancient University in the western world, Bologna, on 10-12 September 2025.

The overarching topic of this year’s conference is ‘Contesting Digital Labor: Resistance, counteruses, and new directions for research’. The goal is to explore how platform workers navigate, challenge, and reshape algorithmic management systems while forging innovative forms of solidarity and collective action. We also aim to explore the perspectives that technological developments open for workers in order to escape everyday surveillance, to resist top-down control and to organise to defend their rights.

In addition to presentations that directly address these questions, we welcome proposals that analyse a broader range of issues related to digital labour.

To read the full Call and submit your abstract, please visit the conference management website.

The deadline is 27 April 2025.

NB: A small number of scholarships to partly cover travel/subsistence costs will be made available – stay tuned for more information.

NB2: The keynotes and plenaries will be announced very soon.

Please feel free to share with any scholars and postgraduate students who might be interested.