De Gruyter Handbook of Critical Data Studies

Ulises A. Mejias is Professor of Communication Studies at the State University of New York at Oswego, and recipient of the 2023 SUNY Chancellor’s Award for Excellence in Scholarship. He is the author of Off the Network: Disrupting the Digital World (University of Minnesota Press, 2013) and, with Nick Couldry, The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism (Stanford University Press, 2019). His latest book, also co-authored with Couldry, is Data Grab: The New Colonialism of Big Tech and How to Fight Back (Chicago University Press, 2024). He serves on the boards of Humanities New York (a National Endowment for the Humanities affiliate) and the Center on Privacy & Technology at Georgetown Law. Dr. Mejias is co-founder of Tierra Común and the Non-Aligned Technologies Movement, two innovation and support networks of activists, educators and scholars working towards the decolonization of data.

Jasmine E. McNealy is a Professor at the University of Florida College of Journalism and Communications where she directs the Infrastructure for Communities Ecology for Data (ICED) Hub. She is an attorney, critical public interest technologist, and social scientist who studies emerging media and technology with a view toward influencing law and policy. Of particular focus for her are the areas of privacy, surveillance, and data governance, and emphasizing technological impacts on marginalized and vulnerable communities.

Milagros Miceli is a sociologist and a computer scientist. She is Research Lead at the Distributed AI Research (DAIR) Institute, Director of the Data, Algorithmic Systems, and Ethics research group at the Weizenbaum Institute, and Professor at the Universidad Nacional de San Martín (Argentina). Her work examines how global data supply chains shape the creation of "ground truth" data and how labor conditions in outsourced data work affect algorithmic outcomes. She is Principal Investigator of Data Workers’ Inquiry, an award-winning project that supports worker-led research into AI data work.