Databite SeriesMay 11 2022

The Automated State

Michele E. Gilman, Joanna Redden, and Ranjit Singh

Databite No. 148

“You can’t cross-examine an algorithm.” —Michele Gilman

What Is the Automated State?
In replacing human workers with machines, the automated state seeks to handle rote and routine work more efficiently. Automation is also aimed at improving the accuracy, fairness, or consistency of decision-making. By removing such decision-making from human discretion, the automated state seeks to depoliticize it (or at least appear to).

But these motivations are anything but neutral: they raise urgent concerns about implementation, oversight, and accountability. What does automation look like in practice? Who and what gets overlooked? What does interacting with systems, interfaces, and datasets require of people, including skills and resources they may not have? Does automation leave room for individual voices? What are the implications for participatory democracy, and for people’s willingness and ability to trust systems of all kinds?

About the Speakers

Ranjit Singh has a doctorate in Science and Technology Studies (STS) from Cornell University. His research lies at the intersection of data infrastructures, global development, and public policy, and uses methods of interview-based qualitative sociology and multi-sited ethnography. Singh examines the everyday experiences of people subject to data-driven practices and follows the mutual shaping of their lives and their data records. His dissertation research on Aadhaar, the national biometrics-based identification infrastructure of India, advances public understanding of the affordances and limits of biometrics-based data infrastructures in practically achieving inclusive development and reshaping the nature of Indian citizenship.

Joanna Redden is an assistant professor at the Faculty of Information & Media Studies at the University of Western Ontario and the co-director of the Data Justice Lab. Her research combines interests in datafication, politics, governance and social justice. Currently, she is working on projects that involve: a) mapping and analyzing the social and political implications of increasing government uses of predictive and automated data systems, b) learning from data harms and those trying to redress these harms, and c) working toward greater citizen participation in our datafied societies.

Michele E. Gilman is the Venable Professor of Law and associate dean for faculty research and development at the University of Baltimore School of Law. Professor Gilman directs the Civil Advocacy Clinic, where she supervises students representing low-income individuals and community groups in a wide range of litigation and law reform matters.  She also teaches evidence, federal administrative law, and poverty law. Professor Gilman writes extensively about data privacy and social welfare issues, and her articles have appeared in journals including the California Law Review, the Vanderbilt Law Review, and the Washington University Law Review.  She was a faculty fellow at Data & Society in 2019–2020, where she focused on legal strategies for countering the harms of data-centric technologies on low-income communities. 




Relevant Content 

Credits and Acknowledgments

Producer: Rigoberto Lara Guzmán

Editorial: Eryn Loeb

Social Media: Alessandra Erawan

Web: Chris Redwood

Post-Production: Kara Constantine

Additional support provided by D&S’s Engagement, Accounting, and Strategy teams.