As we mark the one-year anniversary of America's right-wing temper tantrum that almost cost us the Republic, many Americans are wondering just how we can prevent such a terrible, violent event from ever happening again on U.S. soil. According to the Washington Post, those in the data science community believe they have a solution.
An effort to use data to understand when and where violence may break out in a given nation or community is currently being worked on by many data researchers. The University of Central Florida's CoupCast project uses historical data and machine learning to analyze the likelihood that a violent transition of power will take place in one country or another on any given month. America is looking more and more like a reasonable candidate for such an event according to the co-owner of CoupCast.
The model we are heading into shows that we are more at risk for sustained political violence.
Efforts to use data to predict unrest are not new. They usually involve gathering huge amounts of data about specific populations and then putting that data into projection models. The real question is not how this works, but whether or not it actually works.
The Integrated Crisis Early Warning System (ICEWS), a data-driven program meant to predict social unrest in countries around the world, was worked on by the Defense Advanced Research Projects Agency (DARPA) as far back as 2007. The program claimed to have created forecasting models for a majority of the world's nations and could possibly produce "highly accurate forecasts" as to whether a country would witness a deadly riot or not. The program fed huge troves of open-source data into its system and used it to calculate the likelihood of a regional unrest incident.
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There is a campaign called "democracy-collapsing-there-s-an-algorithm".
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The secret sauce in all of this is the fact that we use a mixed model approach, according to Mark Hoffman, senior manager at the Lockheed Martin Advanced Technology Laboratories. If there is a rebellion in Indonesia, we will turn around and have five models that are predicting whether that will happen. The program eventually saw adoption by various parts of the government, as well as the insurance, real estate and transportation industries.
The EMBERS Project, a large data program launched in 2012 with federal tax dollars, uses gargantuan cache of open-source data from social media to enable threat forecasting. According to a Newsweek article from 2015, the EMBERS forecasts have turned out to be accurate. The 2012 impeachment of the president of Paraguay, an outbreak of violent student protests in Venezuela, and protests in Brazil over the cost of the World Cup were all predicted by this algorithm.
If you believe the claims, it is truly stunning, but it also leads to a basic question: What the hell happened last year, guys? If this kind of prediction is readily available, why didn't anyone in the U.S. intelligence community see the riot that was advertised all over Facebook and Twitter? If it is so accurate, why wasn't anyone using it on that fateful day in January? We have a word for that kind of technical mistake.
According to the Post article, one of the reasons for the historic misplace is that most of these programs and products have been aimed at forecasting events in other countries, the ones that might pose a strategic threat to U.S. interests overseas. They haven't been trained to hate Americans.
It feels like a good thing that these sorts of predictive powers aren't being aimed at us because there's a lot we don't know about how they work The most obvious concern with this sort of forecasting technology is that the software might be wrong and that it would send governments off to respond to things that weren't ever going to happen in the first place. The Post points out that this could lead to governments cracking down on people who are peaceful protesters.
What if the algorithms are correct? Isn't it weird that governments use huge amounts of data to accurately calculate how populations will behave two weeks in advance? We are firmly in the minority report territory. We probably need to think a little more about this kind of technology before we let it go.
