The provocative idea behind unrest prediction is that by designing an artificial-intelligence model that can quantify variables — a country’s democratic history, democratic “backsliding,” economic swings, “social-trust” levels, transportation disruptions, weather volatility and others — predicting political violence can be more accurate than ever.

Some ask whether any model can really process the myriad and often local factors that play into unrest. To those enacting it, however, the science is sufficiently strong and the data now robust enough to etch a meaningful picture. In their conception, the next Jan. 6 won’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.

“Another analogy that works for me is the weather,” said Philip Schrodt, considered one of the fathers of unrest-prediction, also known as conflict-prediction. A longtime Pennsylvania State University political science professor, Schrodt now works as a high-level consultant, including for U.S. intelligence agencies, using AI to predict violence. “People will see threats like we see the fronts of a storm — not as publicly, maybe, but with a lot of the same results. There’s a lot of utility for this here at home.”