Earlier this week, I shared my thoughts on crime prevention through technology with IDG Connect reporter Bianca Wright. Take a look and feel free to share your opinions in the comment section below (edited for length and clarity)!

Earlier this week, I shared my thoughts on crime prevention through technology with IDG Connect reporter Bianca Wright. Take a look and feel free to share your opinions in the comment section below (edited for length and clarity)!

Researchers from the University of Cardiff have been awarded more than $800,000 by the U.S. Department of Justice to develop a pre-crime detection system that uses social media. How would such technology work? Are there other examples of technologies being used in this way?

The particular award for the University of Cardiff was to fight hate crime, and this is an important distinction. Taking a data-driven “predictive policing” approaches to fighting general crime is very difficult because crime itself is so varied, and the dimensions of each type of crime are so complex. However, for hate crimes in particular, social media could be a particularly useful data stream, because it yields insight into a variable that is otherwise extremely difficult to assess: human sentiment. The general mechanism of the system would be to look for patterns and correlations between all the dimensions of social media: text in posts and tweets, captions on images, the images themselves, even which people, topics, organizations someone subscribes to. Metadata on these would also feed into the data modeling; the timestamps and locations of their posts and social media activity can be used to infer where they live, their income, level of education, etc. 

Social media is most powerful when the additional information streams it generates are paired up with existing information about someone. Sometimes unexpected correlations emerge. For instance, could it be the case that among those expressing hate speech in their social media feeds, the people with a criminal background are actually less likely to engage in hate crime, because they already have a rap sheet and know that law enforcement is aware of them, and, instead, most hate crimes are committed by first-time offenders? Ultimately, the hope of social media data science is to be able to get real insight into questions like these, which then can suggest effective remediations and preventative measures.

How viable is such technology in predicting and preventing crime? Is the amount of big data available to law enforcement enough to help them predict a crime before it happens?

It’s hard to say in general. It seems like most common sorts of physical crime are deeply correlated to socioeconomic, geographic and demographic factors. These are things on which we have reasonably large amounts of data.  A challenge there is that many of those datasets are government data, stored in arcane locations and formats across a variety of different bureaucracies and difficult to synthesize. However, past evidence shows if you simply integrate the data that governments already possess, you can get some incredible insights. For instance, Jeff Chen’s work with the Fire Department of New York shows that they can predict which areas have buildings that are more likely to catch on fire, and take preventative actions. 

Ironically, hate crimes may be particularly difficult to actually tackle with data science, because they are a form of domestic terrorism, with highly asymmetric dynamics between perpetrator and potential victims.  One possible result of the University of Cardiff grant, is that we discover that data science and social media can reveal elevated risk of hate crimes in certain areas, but offer insufficient information for taking any kind of preventative or remediative measures.

What are the challenges to such technologies? How do you see this developing in the future?

I think that the breakthroughs in the field of machine learning can lead to better and smarter policy across the board: from crime prevention to international trade to reducing terrorism and extremism. The biggest challenge it faces is that its real technological breakthroughs are mostly mathematical in nature, and not something “concrete” that regular people can readily understand. Some technology breakthroughs are extremely visceral: electrical cars that go from 0-60 in 3 seconds, spacecraft that beam down breathtaking images, and the like. We even have computerized devices that talk to us in natural language. The average person can “get” that these are advances.

Advances in machine learning and data science can deeply improve human civilization, by helping us make better policy, allocate resources better, reduce waste and improve quality of life. 


See the full article in IDG Connect, here