About

Julian Posada is an Assistant Professor of American Studies at Yale University and a Just Tech Fellow at the Social Science Research Council. His research focuses on the social and cultural dimensions of information, with a particular emphasis on the relationship between labor and the development of artificial intelligence. Posada's work critically examines how the labor underpinning artificial intelligence is organized through a form of "platform extractivism," which exploits economic instability to capture value from precarious populations. This concept is central to his forthcoming book, "Platform Extractivism: Data Work and the People Powering Artificial Intelligence," published by the University of California Press in October 2026. The book reframes AI data work as a continuation of historical extractive relations and offers a critical intervention into debates on the future of work and the hidden costs behind digital platforms. Posada's academic contributions have been published in prominent journals such as Big Data & Society, Information, Communication & Society, Frontiers in Artificial Intelligence, and the Proceedings of the ACM on Human-Computer Interaction. His research and commentary have also appeared in major media outlets including The Economist, Fortune, WIRED, the MIT Technology Review, and NBC. His work is supported by organizations such as the International Development Research Centre and the Social Science Research Council, with funding from foundations including MacArthur, Ford, and Surdna. Posada holds a Ph.D. in Information Science from the University of Toronto and has held visiting appointments at the Massachusetts Institute of Technology and the Weizenbaum Institute. He is fluent in Spanish, French, and English.

Research topics

• Social Science
• Sociology
• Computer Science
• Political Science
• Artificial Intelligence
• Computer Security
• Data science
• Epistemology
• Business
• Machine Learning
• Knowledge management
• Marketing
• Engineering
• Ecology
• Anthropology
• Microeconomics
• Economics
• Public relations
• World Wide Web

Selected publications

• AI x Crisis: Tracing New Directions beyond Deployment and Use

  2025-08-18

  articleOpen access

  Faced with multiple, intersecting crises, numerous computing technologies have emerged and interacted with the crises.Amidst the growing prominence of AI, the discourses on AI-related harms predominantly focus on AI deployment and use, shifting attention away from their social and structural underpinnings.In response, this workshop seeks to reflect and map how AI intersects with the crises through framing the costs of AI.With costs of AI we refer to the human and natural toll of AI systems, such as labor exploitation, environmental degradation, and perpetuated social inequality, and emphasize the inherent and inevitable trade-offs in AI development and use.We invite contributions on various forms of AI-related costs, and critical engagement with methods to approach and address these costs.This workshop aims to (1) map the various costs of AI; (2) explore and reflect on concepts, frameworks, and methods to approach and engage with them; and (3) foster exchanges and collaborations in an interdisciplinary community.

  PublisherOA PDFDOI

• Remote robotics, or the digital re-embodiment of labour

  Work Organisation Labour & Globalisation · 2025-01-01 · 1 citations

  articleOpen access

  The remote operation of robots in logistics is becoming increasingly prevalent, with robots being deployed across a variety of sectors and operated by workers from a distance. This allows manual labour to be conducted remotely. Despite eliminating the need for physical proximity between the robot and the operator, remote robotics still necessitates human interaction to control the machinery, a process we call re-embodiment. This working arrangement introduces constraints on the communities and territories of remote workers. Rather than deviating from traditional labour practices, remote robotics extends the reach of capital and perpetuates existing patterns of exploitation.

  PublisherOA PDFDOI

• Factors influencing trust in algorithmic decision-making: an indirect scenario-based experiment

  Frontiers in Artificial Intelligence · 2025-02-04 · 19 citations

  articleOpen access

  Algorithms are involved in decisions ranging from trivial to significant, but people often express distrust toward them. Research suggests that educational efforts to explain how algorithms work may help mitigate this distrust. In a study of 1,921 participants from 20 countries, we examined differences in algorithmic trust for low-stakes and high-stakes decisions. Our results suggest that statistical literacy is negatively associated with trust in algorithms for high-stakes situations, while it is positively associated with trust in low-stakes scenarios with high algorithm familiarity. However, explainability did not appear to influence trust in algorithms. We conclude that having statistical literacy enables individuals to critically evaluate the decisions made by algorithms, data and AI, and consider them alongside other factors before making significant life decisions. This ensures that individuals are not solely relying on algorithms that may not fully capture the complexity and nuances of human behavior and decision-making. Therefore, policymakers should consider promoting statistical/AI literacy to address some of the complexities associated with trust in algorithms. This work paves the way for further research, including the triangulation of data with direct observations of user interactions with algorithms or physiological measures to assess trust more accurately.

  PublisherOA PDFDOI

• Deeply embedded wages: Navigating digital payments in data work

  Big Data & Society · 2024-04-30 · 18 citations

  articleOpen access1st authorCorresponding

  Many workers worldwide rely on digital platforms for their income. In Venezuela, a nation grappling with extreme inflation and where most of the workforce is self-employed, data production platforms for machine learning have emerged as a viable opportunity for many to earn an income in US dollars. Data workers are deeply interconnected within a vast network of entities that act as intermediaries for wage payments in digital currencies. Past research on embeddedness has noted that being intertwined in multi-tiered socioeconomic networks of companies and individuals can offer significant rewards to social participants, while also connoting a particular set of limitations. This paper provides qualitative evidence regarding how this “deep embeddedness” impacts data workers in Venezuela. Given the backdrop of a national crisis and rampant hyperinflation, the perks of receiving wages through financial platforms include accessing more stable currencies and investment outside the national financial system. However, relying on numerous intermediaries often diminishes income due to transaction fees. Moreover, this introduces heightened financial risks, particularly due to the unpredictable nature of cryptocurrencies as an investment. This paper evaluates the effects of the platformization of wages and its effect on working conditions. The over-reliance on external financial platforms erodes worker autonomy through power dynamics that lean in favor of the platforms that set the transaction rules and prices. These findings present a multifaceted perspective on deep embeddedness in platform labor, highlighting how the rewards of financial intermediation often come at a substantial cost for the workers in precarious situations.

  PublisherOA PDFDOI

• The Human Factor in AI Red Teaming: Perspectives from Social and Collaborative Computing

  2024-11-11 · 10 citations

  preprintOpen access

  Rapid progress in general-purpose AI has sparked significant interest in "red teaming,'' a practice of adversarial testing originating in military and cybersecurity applications. AI red teaming raises many questions about the human factor, such as how red teamers are selected, biases and blindspots in how tests are conducted, and harmful content's psychological effects on red teamers. A growing body of HCI and CSCW literature examines related practices-including data labeling, content moderation, and algorithmic auditing. However, few, if any, have investigated red teaming itself. This workshop seeks to consider the conceptual and empirical challenges associated with this practice, often rendered opaque by non-disclosure agreements. Future studies may explore topics ranging from fairness to mental health and other areas of potential harm. We aim to facilitate a community of researchers and practitioners who can begin to meet these challenges with creativity, innovation, and thoughtful reflection.

  PublisherOA PDFDOI

• Deeply Embedded Wages: Navigating Digital Payments in Data Work

  arXiv (Cornell University) · 2024-03-03

  preprintOpen access1st authorCorresponding

  Many of the world's workers rely on digital platforms for their income. In Venezuela, a nation grappling with extreme inflation and where most of the workforce is self-employed, data production platforms for machine learning have emerged as a viable opportunity for many to earn a flexible income in US dollars. Platform workers are deeply interconnected within a vast network of firms and entities that act as intermediaries for wage payments in digital currencies and its subsequent conversion to the national currency, the bolivar. Past research on embeddedness has noted that being intertwined in multi-tiered socioeconomic networks of companies and individuals can offer significant rewards to social participants, while also connoting a particular set of limitations. This paper furnishes qualitative evidence regarding how this deep embeddedness impacts platform workers in Venezuela. Given the backdrop of a national crisis and rampant hyperinflation, the perks of receiving wages through various financial platforms include access to a more stable currency and the ability to save and invest outside the national financial system. However, relying on numerous digital and local intermediaries often diminishes income due to transaction fees. Moreover, this introduces heightened financial risks, particularly due to the unpredictable nature of cryptocurrencies as an investment. The over-reliance on external financial platforms erodes worker autonomy through power dynamics that lean in favor of the platforms that set the transaction rules and prices. These findings present a multifaceted perspective on deep embeddedness in platform labor, highlighting how the rewards of financial intermediation often come at a substantial cost for the workers in unstable situations, who are saddled with escalating financial risks.

  PublisherOA PDFDOI

• Labor, Automation, and Human–Machine Communication

  2023-01-01 · 4 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• The Data-Production Dispositif

  Proceedings of the ACM on Human-Computer Interaction · 2022 · 97 citations

  Senior authorCorresponding
  • Computer Science
  • Sociology
  • Political Science

  Machine learning (ML) depends on data to train and verify models. Very often, organizations outsource processes related to data work (i.e., generating and annotating data and evaluating outputs) through business process outsourcing (BPO) companies and crowdsourcing platforms. This paper investigates outsourced ML data work in Latin America by studying three platforms in Venezuela and a BPO in Argentina. We lean on the Foucauldian notion of dispositif to define the data-production dispositif as an ensemble of discourses, actions, and objects strategically disposed to (re)produce power/knowledge relations in data and labor. Our dispositif analysis comprises the examination of 210 data work instruction documents, 55 interviews with data workers, managers, and requesters, and participant observation. Our findings show that discourses encoded in instructions reproduce and normalize the worldviews of requesters. Precarious working conditions and economic dependency alienate workers, making them obedient to instructions. Furthermore, discourses and social contexts materialize in artifacts, such as interfaces and performance metrics, limiting workers' agency and normalizing specific ways of interpreting data. We conclude by stressing the importance of counteracting the data-production dispositif by fighting alienation and precarization, and empowering data workers to become assets in the quest for high-quality data.

  PublisherOA PDFDOI

• The Data-Production Dispositif

  arXiv (Cornell University) · 2022-05-24 · 13 citations

  preprintOpen accessSenior author

  Machine learning (ML) depends on data to train and verify models. Very often, organizations outsource processes related to data work (i.e., generating and annotating data and evaluating outputs) through business process outsourcing (BPO) companies and crowdsourcing platforms. This paper investigates outsourced ML data work in Latin America by studying three platforms in Venezuela and a BPO in Argentina. We lean on the Foucauldian notion of dispositif to define the data-production dispositif as an ensemble of discourses, actions, and objects strategically disposed to (re)produce power/knowledge relations in data and labor. Our dispositif analysis comprises the examination of 210 data work instruction documents, 55 interviews with data workers, managers, and requesters, and participant observation. Our findings show that discourses encoded in instructions reproduce and normalize the worldviews of requesters. Precarious working conditions and economic dependency alienate workers, making them obedient to instructions. Furthermore, discourses and social contexts materialize in artifacts, such as interfaces and performance metrics, limiting workers' agency and normalizing specific ways of interpreting data. We conclude by stressing the importance of counteracting the data-production dispositif by fighting alienation and precarization, and empowering data workers to become assets in the quest for high-quality data.

  PublisherOA PDFDOI

• Documenting Data Production Processes: A Participatory Approach for Data Work

  arXiv (Cornell University) · 2022-07-11 · 7 citations

  preprintOpen access

  The opacity of machine learning data is a significant threat to ethical data work and intelligible systems. Previous research has addressed this issue by proposing standardized checklists to document datasets. This paper expands that field of inquiry by proposing a shift of perspective: from documenting datasets toward documenting data production. We draw on participatory design and collaborate with data workers at two companies located in Bulgaria and Argentina, where the collection and annotation of data for machine learning are outsourced. Our investigation comprises 2.5 years of research, including 33 semi-structured interviews, five co-design workshops, the development of prototypes, and several feedback instances with participants. We identify key challenges and requirements related to the integration of documentation practices in real-world data production scenarios. Our findings comprise important design considerations and highlight the value of designing data documentation based on the needs of data workers. We argue that a view of documentation as a boundary object, i.e., an object that can be used differently across organizations and teams but holds enough immutable content to maintain integrity, can be useful when designing documentation to retrieve heterogeneous, often distributed, contexts of data production.

  PublisherOA PDFDOI

Frequent coauthors

• Milagros Miceli

  Technische Universität Berlin

  11 shared

• Pierre Caillou

  Université Paris-Sud

  6 shared

• Tianling Yang

  Weizenbaum Institute

  5 shared

• Alexander Verl

  University of Stuttgart

  4 shared

• Antonio A. Casilli

  Télécom Paris

  3 shared

• Ryland Shaw

  3 shared

• Emily Tseng

  Cornell University

  3 shared

• Manuel Drust

  Fraunhofer Institute for Manufacturing Engineering and Automation

  2 shared

Labs

• Posada LaboratoryPI

Education

• PhD in Information Science, Faculty of Information

  University of Toronto

  2022

Awards & honors

• Just Tech Fellowship of the Social Science Research Council