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.

Research Articles

// “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

 

// 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 🏆 

ACM digital library // PDF // video presentation

 

// 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

ACM digital library // PDF // video presentation

 

// 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).

ACM digital library // PDF // video presentation 

 

// 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).

ACM digital library // PDF

 
 

Public Writing

// The Performativity of Ground-Truth Data.

Milagros Miceli & Tianling Yang.

2023, unthinking.photography

 

// Data Work and its Layers of (In)Visibility.

Adrienne Williams & Milagros Miceli.

2023, Just Tech

 

// La explotación laboral detrás de la inteligencia artificial

Adrienne Williams, Milagros Miceli, and Timnit Gebru.

2023, DataGénero

 

// The Exploited Labor Behind Artificial Intelligence.

Adrienne Williams, Milagros Miceli, and Timnit Gebru.

2022, Noema Magazine

 

(upcoming) Talks

2024-04-18 – Machtasymmetrien und Arbeitsbedingungen in der Datenarbeit // LOOPS // UdK Berlin

2024-03-20 – On the Power Dynamics and Labour Conditions on ML Data Production // University of Amsterdam
2024-01-18 – Data & Society — Generative AI’s Labor Impacts
2024-01-12 – Platform work and AI // Institute for Human Development

2023-12-08 – She, He and It. Discussing AI and Journalism from a Global South Perspective. // Syrian Female Journalist Network // Oyoun
2023-12-06 – AI and the future (and present) of work //  ReDemocracIA
2023-11-10 – Data Work: Classifying, Naming, Exerting Power // Viadrina University
2023-11-09 – Responsibly Working with Crowdsourced Data // HCOMP
2023.11.04 – Harvest and Decay: A weekend on Artificial intelligence // Gropius Bau
2023-10-24 – There’s no Ethical AI without Ethical Data Work // John Carbot University
2023-09-29 – The Labor that Fuels AI // Deutsches Haus // NYU
2023-06-28 – Transparency for whom? Designing data documentation with data workers // NoBias Data School
2023-06-14 – We need to talk about data work for machine learning // Nexa Center // Turin University
2023-06-12 – Responsibly Working with Crowdsourced Data // FAccT 2023
2023.06.02 – Transparency for whom? Designing data documentation with data workers // DGTF
2023-06-01 – Designing data documentation with data workers // Science of Intelligence //  TU Berlin
2023-05-26 – We need to talk about Labour as a central dimension of AI ethics // TU Munich
2023-04-04 – ¿Por qué es necesario hablar de trabajo cuando hablamos de IA? // Unversidad Torcuato Di Tella
2023-03-29 – Poder sesgos en el trabajo de datos // <A+> Alliance for Inclusive Algorithms
2023-03-17 – On Worker Exploitation, Data Theft, and the Centralization of Power // Panel at Stochastic Parrots Day

© Milagros Miceli 2024