Dr. Milagros Miceli
I am a sociologist and computer scientist who investigates how ground-truth data for machine learning is produced. The focus of my research are labor conditions and power dynamics in data generation and labeling. Broadly, I am interested in questions of meaning-making, knowledge production, and symbolic power encoded in ML data. My work comprises ethnographic fieldwork, interviews, and participatory engagements with data annotators, collectors, and scientists at several sites around the world.
I lead the newly funded research group Data, Algorithmic Systems, and Ethics at Weizenbaum-Institut. I also work as a researcher at DAIR Institute where I am thinking through ways of engaging communities of data workers in AI research.
I am also a mom, an immigrant, and a first-generation academic. My pronouns are she/ella.
Selected Publications
// The Exploited Labor Behind Artificial Intelligence
Adrienne Williams, Milagros Miceli, and Timnit Gebru.
2022
// Documenting Data Production Processes. A Participatory Approach to Data Work.
Milagros Miceli, Tianling Yang, Adriana Alvarado García, Julian Posada, Sonja Mei Wang, Marc Pohl, and Alex Hanna.
In Proc. ACM Hum.-Compt. Interact. 2022
ACM digital library // arXiv // prototype // video presentation
// The Data-Production Dispositif.
Milagros Miceli and Julian Posada.
In Proc. ACM Hum.-Compt. Interact. 2022
Honorable Mention, Methods Award, Impact Award
ACM digital library // PDF // arXiv // poster // blog post // video presentation
// Algorithmic Tools in Public Employment Services: Towards a Jobseeker-Centric Perspective.
Kristen M. Scott, Sonja Mei Wang, Milagros Miceli, Pieter Delobelle, Karolina Sztandar-Sztanderska, and Bettina Berendt.
In ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). 2022.
Best Paper Award
// Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?
Milagros Miceli, Julian Posada, and Tianling Yang.
In Proc. ACM Hum.-Compt. Interact. 2022
// Wisdom for the Crowd: Discursive Power in Annotation Instructions for Computer Vision.
Milagros Miceli and Julian Posada.
CVPR 2021 Workshop Beyond Fairness: Towards a Just, Equitable, and Accountable Computer Vision.
arXiv // PDF // poster // video presentation
// Documenting Computer Vision Datasets: An Invitation to Reflexive Data Practices
Milagros Miceli, Tianling Yang, Laurens Naudts, Martin Schuessler, Diana Serbanescu, and Alex Hanna.
In Conference on Fairness, Accountability, and Transparency (FAccT ’21).
// Between Subjectivity and Imposition: Power Dynamics in Data Annotation for Computer Vision.
Milagros Miceli, Martin Schuessler, and Tianling Yang.
In Proc. ACM Hum.-Comput. Interact. 4, CSCW2 (October 2020).
Best Paper Award
ACM digital library // PDF // video presentation // blog post
// Biased Priorities, Biased Outcomes: Three Recommendations for Ethics-oriented Data Annotation Practices.
Gunay Kazimzade and Milagros Miceli.
In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES ’20).