Data and Society
A reflection on the datafied state, surveillance technologies, and the importance of evaluating data systems in social context.
Conversations on the Datafied State Part Three: Race, Surveillance, Resistance
The talk explores how data-driven governance can connect public safety, surveillance, and inequality. It asks how technology-mediated institutions shape the lives of communities that are already subject to unequal scrutiny.
The speakers include Tamara K. Nopper, whose work examines technology and inequality; Chaz Arnett, who researches state surveillance; Raul Carrillo, who studies finance and technology; and Alyx Goodwin, who works on the relationship among finance, policing, and technology.
The "datafied state" refers to the adoption of data and algorithms for state purposes. These systems can make governance more efficient, but they can also embed existing institutional priorities and biases. The speakers emphasize that technology is not neutral: power shapes whose data is collected, how it is interpreted, and what actions follow.
ShotSpotter is one example discussed in the talk. It is marketed as a gun violence detection system, but critics argue that false positives and uneven deployment can increase police contact in communities that are already over-surveilled. This example shows why evaluating a technology requires more than asking whether it works technically. We also need to ask where it is deployed, who is affected, and what harms may follow from false or ambiguous outputs.
My question is: to what degree can data be used in justice-oriented work without reproducing the same systems of control it critiques?
This question matters to me because I am interested in computational social science, but I am cautious about treating quantitative methods as automatically objective. Data can reveal patterns of inequality, but without interpretation and community engagement it can become purely descriptive. It can show that millions of people face food insecurity, for example, while still failing to explain the lived experience behind that number.
The talk was useful because it treated technology as part of a broader social system. It reminded me that data ethics should include history, institutional context, and the people most affected by technical systems.