Big Data Surveillance
Newly minted sociologist Sarah Brayne spent two and a half years studying the LAPD as it shifted from traditional methods to what she calls big data surveillance.
This article examines the intersection of two structural developments: the growth of surveillance and the rise of “big data.” Drawing on observations and interviews conducted within the Los Angeles Police Department, I offer an empirical account of how the adoption of big data analytics does—and does not—transform police surveillance practices. I argue that the adoption of big data analytics facilitates amplifications of prior surveillance practices and fundamental transformations in surveillance activities. First, discretionary assessments of risk are supplemented and quantified using risk scores. Second, data are used for predictive, rather than reactive or explanatory, purposes. Third, the proliferation of automatic alert systems makes it possible to systematically surveil an unprecedentedly large number of people. Fourth, the threshold for inclusion in law enforcement databases is lower, now including individuals who have not had direct police contact. Fifth, previously separate data systems are merged, facilitating the spread of surveillance into a wide range of institutions. Based on these findings, I develop a theoretical model of big data surveillance that can be applied to institutional domains beyond the criminal justice system. Finally, I highlight the social consequences of big data surveillance for law and social inequality.
Here’s one bit, not far from what one would see on CSI:
For example, after a series of copper wire thefts in the city, the police found the car involved by drawing a radius in Palantir
around the three places the wire was stolen from, setting up time bounds around the time they knew the thefts occurred at each site, and
querying the system for any license plates captured by ALPRs in all three locations during those time periods.
I encountered several other examples of
law enforcement using external data originally
collected for non–criminal justice purposes,
including data from repossession and collections
agencies; social media, foreclosure, and
electronic toll pass data; and address and
usage information from utility bills. Respondents
also indicated they were working on
integrating hospital, pay parking lot, and
university camera feeds; rebate data such as
address information from contact lens rebates;
and call data from pizza chains, including
names, addresses, and phone numbers from
Papa Johns and Pizza Hut. In some instances,
it is simply easier for law enforcement to purchase
privately collected data than to rely on
in-house data because there are fewer constitutional
protections, reporting requirements,
and appellate checks on private sector surveillance
and data collection (Pasquale 2014).
Moreover, respondents explained, privately
collected data is sometimes more up-to-date.
Hat tip: Kevin Lewis.