Dr. Milagros Miceli

I am a sociologist and computer scientist investigating how ground-truth data for machine learning is produced. My research focuses on labor conditions and power dynamics in data work. Since 2018, I have continuously engaged with communities of data workers globally.

I am research lead at the DAIR Institute, head of the Data, Algorithmic Systems, and Ethics research unit at Weizenbaum-Institut, and a lecturer at TU Berlin. I am also the principal investigator of the Data Workers‘ Inquiry project, a space for data workers to actively engage in AI research. 

I am also a mom, an immigrant, and a first-generation college graduate and academic. My pronouns are she/ella.

Selected Research Articles

// What Knowledge Do We Produce from Social Media Data and How?

Adriana Alvarado, Tianling Yang, Milagros Miceli (*joint first authors). In Proc. ACM Hum.-Compt. Interact (GROUP). 2025.
Best Paper Award 🏆 

// PDF

// Lost in Machine Translation: The Sociocultural Implications of Language Technologies in Nigeria.

Seyi Olojo, Janina Zakrzewski, Andrew Smart, Erin van Liemt, Milagros Miceli, Amber Ebinama, and Lameck Mbangula Amugongo. In ACM Conference on Fairness, Accountability, and Transparency (FAccT). 2025.

// ACM digital library

// The Making of Performative Accuracy in AI Training: Precision Labor and Its Consequences. 

Ben Zefeng Zhang, Tianling Yang, Milagros Miceli, Oliver L. Haimson, and Michaelanne Thomas. In Proc. ACM Hum.-Compt (CHI). Interact. 2025.

// ACM digital library

// The Role of Expertise in Effectively Moderating Harmful Social Media Content.

Nuredin Ali Abdelkadir, Tianling Yang, Shivani Kapania, Meron Estefanos, Fasica Berhane Gebrekidan, Zecharias Zelalem, Messai Ali, Rishan Berhe, Dylan Baker, Zeerak Talat, Milagros Miceli, Alex Hanna, and Timnit Gebru In Proc. ACM Hum.-Compt (CHI). Interact. 2025.

// ACM digital library

// “Guilds” as Worker Empowerment and Control in a Chinese Data Work Platform.

Tianling Yang and Milagros Miceli. In Proc. ACM Hum.-Compt. Interact. 2024.

// PDF

 

// Who trains the data for European artificial intelligence?

Milagros Miceli, Paola Tubaro, Antonio A. Casilli, Thomas Le Bonniec, Camilla Salim Wagner, Laurenz Sachenbacher. European Parliament; The Left. 2024.

// PDF

 

// “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

// 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 // PDF // 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 // 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 // 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

// How we made Data Workers‘ Inquiry

Milagros Miceli.

2025, DWI

 

// “I hope this isn’t for weapons.” How Syrian Data Workers Train AI

Milagros Miceli.

2024, Untold Mag

 

// 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

2025

 

 
2025-07-17 – Keynote at African Computer Vision Summer School // online
 
2025-07-14 – Panel: Who Owns AI? // Publix, Berlin
 
2025-06-27 – Keynote: A Data Workers‘ Inquiry // Countervailing Platform Power // Scuola Normale Superiore
 
2025-06-18 – Keynote and roundtable with data workers // IBEI, Barcelona

© Milagros Miceli 2024