Brit boffins get $800k for Los Angeles Twitter pre-crime tech
Department of Justice cash will develop Minority Report-style tool using social media data
Researchers from the University of Cardiff have been awarded more than $800,000 by the US Department of Justice to develop a pre-crime detection system.
Boffins from the University’s Social Data Science Lab, which brings together a range of scientists to study the methodological, theoretical, empirical and technical dimensions of data analytics in social policy contexts, will tackle hate crime in Los Angeles by developing a statistical tool that uses social media information to make real-time predictions of where hate crimes are likely to occur.
The team plans to analyse data from Twitter and cross-reference it with LAPD data on reported hate crimes "to develop markers, or signatures, which could indicate if, and where, a hate crime is likely to take place at a certain point in time," and thus enable the boys in blue to intervene.
It is reportedly the first time that social media has been used in the US to create predictive policing models for preventing hate crime, defined by the researchers as a "prejudice-motivated crime, often violent, which occurs when a perpetrator targets a victim because of his or her affiliation to a social group, such as their sex, ethnicity, disability or religion."
The LAPD has been excited about predictive policing for quite some time, with a paper on Randomized Controlled Field Trials of Predictive Policing published in the Journal of the American Statistical Association last year.
The paper used data from the LAPD and from Kent Police in the UK to establish a method for identifying crime hotspots and reduce its occurrence by deploying police officers there ahead of time.
Predictive policing has been criticised, however. Critics allege that the creation of crime models from existing data damages the capability of police forces to react to new or novel criminal occurrences and instead encourages confirmation bias in policing techniques.
Faiza Patel, the co-director of the Liberty and National Security Program at the Brennan Center for Justice at New York University Law School, wrote a warning article in the New York Times about the need for police to distinguish statistical probability from reasonable suspicion:
At a time of rising concern about over-policing in minority communities, surging police to particular locations may have its own compounding negative consequences. Technology that purports to zero in on categories of people likely to commit crimes is even more suspect. It undermines the constitutional requirement that police should target people based upon an individual suspicion of wrongdoing, not statistical probability.
Previous research from the Social Data Science Lab boffins at Cardiff had found that Twitter data could be used to identify hot spots, such as certain states or cities, where hate speech has occurred but where hate crime has not been reported, for example in areas where recent immigrants may be unlikely to report crimes against them due to fear of deportation.
Professor Matt Williams, from the University’s School of Social Science, said: “Developing a better understanding of hateful sentiments online and their relationship with crime on the streets could push law enforcement to better identify, report and address hate crimes that are occurring offline.”
Dr Pete Burnap, from Cardiff's School of Computer Science and Informatics, said: “This is the first study in the United States to use social media data in predictive policing models of hate crime. Predictive policing is a proactive law enforcement model that has become more common partially due to the advent of advanced analytics such as data mining and machine-learning methods.”
“New analytic approaches and the ability to process very large data sets have increased the accuracy of predictive models over traditional crime analysis methods and this project will evaluate if police departments can leverage these new data and techniques to reduce hate crimes.” Burnap added. ®