data-workers.orgResearch PaperResearch & Papers
- Authors
- Milagros Miceli, Alex Hanna, Timnit Gebru
- Venue
- AIES (AAAI/ACM Conference on AI, Ethics, and Society)
- Published
- August 2025
The methodology paper behind Data Workers' Inquiry, by Milagros Miceli, Alex Hanna, Timnit Gebru and colleagues at the Distributed AI Research Institute. It lays out a participatory framework for centering workers' own questions and epistemic authority in AI research, turning hidden and precarious labor into a site for knowledge-making and change.
#ai#data-labeling#labor
data-workers.orgAdded Oct 1, 20260 opens data-workers.org WebsiteTechnology & Ethics
A global participatory research initiative spanning nine countries where data workers themselves become community researchers, documenting labor conditions in the AI industry through zines, documentaries, comics, essays, podcasts and animations. Inquiries cover workers at Sama, CloudFactory and Remotasks in Kenya, Venezuela, Syria, Brazil, France and Germany.
#ai#data-labeling#labor
data-workers.orgAdded Oct 1, 20260 opens 
snorkel.aiPublicationTechnology & Ethics
Vendor research The free research library and blog of Snorkel AI, the company spun out of Stanford's Snorkel project on programmatic labelling. The papers and posts explain how training data for AI models is actually built — labelling, evaluation sets, and the 'environments' used to train agents. Useful if you want to understand the unglamorous data work behind model quality, which is where a lot of the real jobs are.
Why I recommend it: Research and blog posts are free to read with no signup. Read it for the how, not the verdict: Snorkel sells data services to frontier AI labs, so posts arguing that better data beats bigger models are also a sales case. Everything else on the site is a paid enterprise product — 'request dataset samples' means a sales call.
#ai-literacy#ai-research#careers-in-ai
snorkel.aiAdded Sep 23, 20260 opens data-workers.org WebsiteTechnology & Ethics
Worker-led research and mental-health resources by and for the data annotators who label the material AI systems learn from.
Why I recommend it: Written by the workers themselves, not about them.
#ai#ai ethics#data-labeling
data-workers.orgAdded Sep 16, 20260 opens 
datalabelers.orgOrganizationTechnology & Ethics
An association organizing the data-labeling workforce behind AI training data around pay, conditions and recognition.
Why I recommend it: A concrete answer to "who actually built this model" — and what they were paid.
#advocacy#ai#ai ethics
datalabelers.orgAdded Sep 16, 20260 opens 