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.
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.
Company that supplies training data, evaluations and AI systems to labs, businesses and governments.
Why I recommend it: A company site, not a course. Useful for understanding the AI data industry; its products are paid and enterprise-only. Scale has faced reporting on contractor pay and conditions.
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.
