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
// “We try to empower them” – Exploring Future Technologies to Support Migrant Jobseekers
Sonja Mei Wang, Kristen M. Scott, Margarita Artemenco, Milagros Miceli, and Bettina Berendt.
In ACM Conference on Fairness, Accountability, and Transparency (FAccT ’23). 2023.
ACM digital library // PDF
// Mobilizing Social Media Data: Reflections of a Researcher Mediating between Data and Organization
Adriana Alvarado García, Marisol Wong-Villacres, Milagros Miceli, Tianling Yang, Benjamín Hernández, and Christopher Le Dantec.
In Proc. ACM Hum.-Compt. Interact. 2023
ACM digital library // PDF
// 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).