DiPLab’s report showing how Outlier tracks the workers that train Big Tech’s AI models was released yesterday, and the media have already started picking up on the story. This is how the story was covered in Spain, France, Norway, Poland and Denmark so far. Klassekampen (Norway) : “– American companies like Meta, Open Al and XAI are using Scale AI‘s platforms to exploit young, newly educated Europeans as a workforce to train their AI models” mediapart.fr (France) : “according to the conclusions of a report released on October 16, Outlier, a subsidiary of the American company Scale AI, itself a pioneer of the annotation since 2016, subjects its workers to extreme surveillance, harvesting and exploiting a very large number of their personal data.” Version2.dk (Denmark) : “In order to be able to work on the platform at all, the workers are forced to provide sensitive biometric data and facial scans through an invasive third-party app called Persona, which is owned by Peter Thiels Founders Fund.” Wyborcza.pl (Poland) adds : “a market has quietly emerged in which Outlier exploits highly skilled Europeans for not particularly high wages while simultaneously building a system of constant surveillance and data extraction that can be used for both AI training and commercial or military purposes” El Pais (Spain) concludes : “The authors of the report believe that Outlier is in breach of European regulation as it is today. They believe that the deregulation promoted by the Commission could weaken GDPR articles that function today as firewalls that stop the transfer of data and knowledge to the United States.”

DiPLab is releasing a report on the surveillance of data workers today. Entitled “Surveillance at Scale : How AI training platforms gather personal data from European workers” this report is a research project in collaboration with Reversing.works and Intérêt à Agir, funded by the Digital Freedom Fund and the European Artificial Intelligence & Society Fund. International technology companies collect vast amounts of personal data from European citizens. Public debate and policymaking often focus on data taken from consumers through e-commerce, social media, or streaming platforms. Yet many Europeans also use digital platforms to work and train AI systems. Online freelance platforms are usually discussed in terms of low pay and insecure “gigs”. Much less attention is given to another resource these platforms extract from workers: their personal data. This report examines the case of Outlier, a key data work platform for artificial intelligence. Outlier is operated by Scale AI, a company that recruits workers to perform tasks such as evaluating AI-generated responses, creating training data, and improving AI models. This report shows how digital platforms rely on data workers worldwide, including in the EU, to label, check, and organize training data. These data are harvested online. But Outlier also collects workers’ personal data, financial records, biometric information, and even facial recognition scans. This data is used for surveillance purposes, and probably for further AI training purposes. Our interdisciplinary team analyzes the evidence collected from qualitative interviews, network logs, and guidelines, to provide evidence of a large-scale data collection process, through hidden trackers. We also provide a legal analysis in terms of likely European law (GDPR) data protection violations : the AI WORLd project reveals how the EU has become a periphery for data extractivism in the production process of AI models. Ethical AI requires the ethical treatment of the modern-day factory workers whose work makes it possible. Beyond the economic exploitation data workers through inadequate compensation, this report reveals the extraction of personal data for purposes ranging from covert AI training to potential military applications. These practices raise serious concerns about social justice, labor law, and European data security. Surveillance_at_scale_diplab_october_2026Download To site this report : Thomas Le Bonniec , Antonio A. Casilli, Mathilde Abel, Simone Robutti, Gaetano Priori, Donatella D., Paul S. Surveillance at Scale : How AI Training Platforms Gather Personal Data from European Workers DiPLab / Reversing.Works report, 2026.

Utopia and Work: When Alternatives Become Possible, a collective volume edited by Mariagrazia Cairo Crocco, Enrico Donaggio, and Nadine Richez-Battesti, was published by Theseo on September 30. In his chapter, “Utopies concrètes et utopies nécessaires à l’heure des intelligences artificielles” (“Concrete utopias and necessary utopias in the age of artificial intelligence”), Antonio Casilli develops four possible utopias for data work, drawing on forms of resistance and agentivity already emerging among workers. Despite the harsh working conditions faced by data workers, the intense surveillance to which they are subjected, and the strict prescription of their work through algorithmic management, data workers engage in both individual and collective forms of resistance to platforms. Workers are resorting to different means of resistance to resist platform exploitation. These include strikes by drivers and delivery workers across Europe, the United States, and Asia, as well as legal action initiated by data workers in Eastern Africa. Through these different forms of action, workers have secured new rights and protections, including the reclassification of employment contract, access to social protections in cases of illness or work related accidents, or even minimal hours payment. Resistance can also be found in free labour done by platform and social medias users with boycott, consumers strikes and consumers unions. As platform workers and users increasingly repurpose platform architectures as tools of resistance, a key question emerges: How do workers invent new forms of autonomy and resistance beyond the constraints imposed by platform capitalism? This chapter builds on one decade of research conducted by Antonio Casilli and DiPLab on data work across the world. It proposes four concrete utopias, possibilities for alternative futures grounded in existing practices of resistance and collective struggle, complemented by two necessary utopias to counterbalance the limitations of the first four. These four concrete utopias are: (1) unionization, whether through data workers joining existing trade unions or creating their own; (2) platform cooperativism and the development of international alternative networks platforms; (3) legislative regulation and the adoption of rules governing platform work; and (4) accountability on the part of technology companies and platforms, through partnerships with their clients and regulatory bodies. The first of the two necessaries utopia is a Social digital income : aka the collective distribution of the money created trough users activities on platforms. The goal is to recognize the free labour users are providing to platforms by using them and also to ensure data workers are remunerated correctly The second is the re-territorialization of digital labour, which involves the collective reappropriation and local governance of digital platforms and infrastructures, with the aim of strengthening workers’ digital sovereignty and their control over the technologies that shape their working conditions. You can download the book for free here

Antonio Casilli’s new chapter for the edited volume Digital Labour in a Turbulent Era, edited by Paola Tubaro, has been pre-published. The book will be published in November of this year. Entitled “The Making of the Data Working Class,” the chapter marks a “turning point” for its author: public awareness of data workers is growing rapidly, and they are starting to fight back and resist. Casilli starts the chapter by describing two categories of workers supporting AI production. The first one consists of tech workers, such as data scientists, data analysts, and software engineers, who are also described as the “AI middle class.” They are highly educated and enjoy a relatively privileged position in the social hierarchy. In contrast, a vast pool of precarious workers operates in the shadows. These “digital blue-collar workers,” who train models, label data, and moderate platform content, are not only rendered invisible but also silenced by corporate non-disclosure clauses. Their position is further shaped by the fact that many of them are located in the Global South. Corrected version He identifies three distinct processes that prefigure the development of a collective class identity: First, the proliferation of academic concepts used to define data work and data workers. Second, the process through which data workers and civil society have adopted, adapted, and reappropriated these definitions. And finally, the historical perspective in which these two processes are embedded, and the ways in which this new labour class has been politically conceptualized over the past decades. He argues that the shifting concepts used by scholars and the proliferation of names adopted by workers themselves are both signs of an ongoing process of class identity formation. Read the chapter here Casilli, Antonio A. (2026), ‘The Making of the Data Working Class’, in P. Tubaro (ed.) Digital Labour in a Turbulent Era, Cheltenham, UK: Edward Elgar Publishing, at HAL science

Edemilson Paraná invites Paola Tubaro to participate in a special issue published in the journal Globalizations that critically interrogates the convergence of environmental sustainability and digitalization. Governments and multi-lateral institutions frame the “twin transition” as a strategy to foster economic growth and durable social prosperity at the same time. However, Paraná argues in his introductory article (co-authored with Rodrigo Santaella-Gonçalves), this agenda is shaped by market logics and geopolitical competition, in ways that ultimately contradict the social and ecological goals it supposedly supports. The special issue has been designed to offer a systemic political-economy perspective on this inconsistency, grounded in critical social sciences and heterodox economics. Overall, it shows how the structural drivers of digitalisation can constrain its green potential and identifies pathways for redirecting the transition towards socially equitable and ecologically sustainable transformations. Paola Tubaro’s article within the issues aims to reconcile two separate trends in the study of the impacts of artificial intelligence (AI), one focused on the natural and the other on the social surroundings that supply resources for its production and use. She introduces the concept of the ‘dual footprint’ as a heuristic device to capture the commonalities and interdependencies between them. For that, she uses two in-depth case studies, each illustrating international flows of raw materials (nature) and of labour services (society). Case studies are a preliminary illustration of a broader idea, that will need to be developed further: that the AI industry is a value chain that spans national boundaries and perpetuates inherited global inequalities. Specifically, she uses the case of Argentina as a provider to the United States and Madagascar as a provider to France, Japan and South Korea. It appears that the countries that drive AI development (here, the United States, France, Japan and South Korea) generate a massive demand for inputs and trigger social costs that, through the value chain, largely fall on more peripheral actors (here, Argentina and Madagascar). While Argentina and Madagascar differ under many respects, in both cases initial hopes of jobs and prosperity have failed to materialise as predominant outsourcing arrangements reinforce informality and precariousness without preventing environmental damage. Put differently, the arrangements in place distribute the costs and benefits of AI unequally, resulting in unsustainable practices and preventing the upward mobility of relatively more disadvantaged countries. The concept of the dual footprint is useful because it grasps how the environmental and social dimensions of the dual footprint emanate from similar underlying socio-economic processes and geographical trajectories. To freely access the article click here

Public debate about work tends to fixate on numbers: jobs created, jobs lost, jobs threatened by automation. Job Quality in a Turbulent Era, a new volume edited by Agnieszka Piasna and Janine Leschke (Edward Elgar Publishing), asks a harder question: what is actually happening to the quality of work? The full book is available in open access via Edward Elgar Publishing. DiPLab’s Antonio A. Casilli and Paola Tubaro contribute a chapter examining how AI and platformization are “fissuring” contemporary workplaces: fragmenting tasks, eroding protections, and reshaping job quality across sectors in ways that aggregate employment statistics simply cannot capture. The volume brings together a wide range of perspectives on how gender, precarity, migration, technology, and environmental conditions are reconfiguring work in an era of ongoing economic turbulence.