â€œWe now have the data â€” and opportunity â€” to pursue a very different path than we did before,â€ said Clayton Besaw, who helps run CoupCast, a machine-learning-driven program based at the University of Central FloridaÂ that predicts the likelihood of coups and electoral violence for dozens of countries each month.
The efforts have acquired new urgency with the recent sounding of alarms in the United States. Last month, three retired generalsÂ warnedÂ in a Washington Post op-ed that they saw conditions becoming increasingly susceptible to a military coup after the 2024 election. Former president Jimmy Carter, writing in the New York Times, sees a country thatÂ â€œnow teeters on the brink of a widening abyss.â€Â Experts have worried aboutÂ various forms of subversion and violence.
The provocative idea behind unrest prediction is that by designing an AI model that can quantify variables â€” a countryâ€™s democratic history, democratic â€œbacksliding,â€ economic swings,Â â€œsocial-trustâ€Â levels, transportation disruptions, weather volatility and others â€” the art of predicting political violence can be more scientific than ever.
Some ask whether any model can really process the myriad and often local factors that play into unrest. To advocates, however, the science is sufficiently strong and the data robust enough to etch a meaningful picture. In their conception, the next Jan. 6 wonâ€™t come seemingly out of nowhere as it did last winter; the models will give off warnings about the body politic as chest pains do for actual bodies.
â€œAnother analogy that works for me is the weather,â€ said Philip Schrodt, considered one of the fathers of unrest-prediction, also known as conflict-prediction. A longtime Pennsylvania State University political scienceÂ professor, Schrodt now works as a high-level consultant, including for U.S. intelligence agencies, using AI to predict violence. â€œPeople will see threats like we see the fronts of a storm â€” not as publicly, maybe, but with a lot of the same results. Thereâ€™s a lot of utility for this here at home.â€
CoupCast is a prime example. The United States was always included in its model as a kind of afterthought, ranked on the very low end of the spectrum for both coups and election violence. But with new data from Jan. 6, researchers reprogrammed the model to take into account factors it had traditionally underplayed, like the role of a leader encouraging a mob, while reducing traditionally important factors like long-term democratic history.
Its risk assessment of electoral violence in the United States has gone up as a result. And although data scientists say Americaâ€™s vulnerability still trails, say, a fragile democracy like Ukraine or a backsliding one like Turkey, itâ€™s not nearly as low as it once was.
â€œItâ€™s pretty clear from the model weâ€™re heading into a period where weâ€™re more at risk for sustained political violence â€” the building blocks are there,â€ Besaw said.Â CoupCastÂ was run by a Colorado-based nonprofit called One Earth Future for five years beginning in 2016 before being turned over to UCF.
Another group, the nonprofitÂ Armed Conflict Location & Event Data Project, or ACLED, also monitors and predicts crises around the world, employing a mixed-method approach that relies on both machine-learning and software-equipped humans.
â€œThere has been this sort of American exceptionalism among the people doing prediction that we donâ€™t need to pay attention to this, and I think that needs to change,â€ said Roudabeh Kishi, the groupâ€™s director of research and innovation. ACLED couldnâ€™t even get funding for U.S.-based predictions until 2020, when it began processing data in time for the presidential election. In October 2020, itÂ predicted an elevated risk for an attack on a federal building.
Meanwhile,Â PeaceTech Lab, a D.C.-based nonprofit focused on using technology in resolving conflict, will in 2022 relaunch Ground Truth, an initiative that uses AI to predict violence associated with elections and other democratic events. It had focused overseas but now will increase efforts domestically.
â€œFor the 2024 election God knows we absolutely need to be doing this,â€ said Sheldon Himelfarb, chief executive of PeaceTech. â€œYou can draw a line between data and violence in elections.â€
â€œThere are so many interacting variables,â€ said Jonathan Powell, an assistant professor at UCF who works on CoupCast. â€œA machine can analyze thousands of data points and do it in a local context the way a human researcher canâ€™t.â€
Many of the models, for instance, find income inequality not to be correlated highly with insurrection; drastic changes in the economy or climate are more predictive.
See FULL STORY at Washington Post.