The International Network on Digital Labor (INDL) is pleased to announce its first symposium in North America, which will be held at Yale University in New Haven, Connecticut, United States, on April 29, 2026. 

The theme for this inaugural North American symposium, “Workers Building, Using, and Resisting AI,” highlights the essential role of labor not only in creating AI models but also in shaping how those technologies reshape work, sustaining labor practices, and developing local strategies to navigate, adapt to, or push back against AI‑driven change. The symposium adopts a broad interpretation of labor’s role in artificial intelligence and related technologies, encompassing the infrastructures and hardware of AI—from manufacturing and assembly to data‑center operations—as well as the many roles involved in its development, from engineering to data work. AI applications are also considered broadly, ranging from computer vision and large‑language models to recommender systems and other reinforcement‑learning‑based tools.

INDL-NA seeks submissions that discuss the role of workers and labor at any point in the extensive AI data pipeline, whether examined from a global or local perspective, and whether focused on data, hardware,infrastructure, models, and their usage. Submissions are welcomed on traditional INDL topics, including:

• Digital labor and AI

• Worker surveillance and algorithmic management

• Governance, regulation, and legislation in AI-related digital labor

• Worker resistance, collective action, refusal, and alternatives

• Automation

As well as on emerging themes within INDL such as:

• The role of women and gender in AI‑related occupations

• Global aspects of labor in the development and uses of AI

• Environmental challenges of labor and AI

• The influence of management and corporations on labor’s persistence in AI

We invite contributions from both established and early‑career academic researchers (including graduate students), as well as from all professionals engaged with these themes including labor organizers, practitioners, and others. All disciplines that study labor and/or technology are welcome, for example economics, management, political science, law, sociology, psychology, history, geography, science‑and‑technology studies (STS), media studies, design, and information and computer sciences.

We are pleased to offer a limited number of scholarships for graduate students, non-academics, and independent scholars. These scholarships and the symposium are funded by Yale’s MacMillan Center. 

This in-person symposium will take place the day after Yale’s university‑wide AI symposium, and participants are welcome to attend that event as well (registration details will be sent separately). 

To submit, please click on "Abstract Submission" in the menu on the left (a free account on this website is required).

Abstracts should:

• Have a maximum length of 500 words

• Be written in English

Please remember to specify:

• Your name, affiliation, and email

• Title

• Abstract (including research objective, methodology, main findings and/or theoretical development, and where relevant, contribution to understanding worker organizing and resistance in digital labor)
  

• You have the option to add a comment or a supporting file if needed.

Abstract submission deadline: January 18, 2026 (Anywhere on Earth).

Notification of acceptance: February 28, 2026

Founded in 2019, the International Network on Digital Labor’s mission is to organize international conferences on digital labor studies and to foster global‑level collaboration between academia and civil society. INDL has organized eight international conferences and has hosted regional symposia in Europe and the Middle East. INDL's next international conference will be at the International Labour Organization in Geneva, Switzerland, in September 2026 and the call for papers will be issued separately. Please note that acceptance to the INDL-NA symposium does not preclude acceptance to the main international INDL conference, as the two events are run separately.