Can AI spot countries at risk of a sudden change in leadership?

What’s new: Researchers at the University of Central Florida are working with a system called CoupCast to estimate the likelihood that an individual country will undergo a coup d’état, The Washington Post reported.

How it works: CoupCast predicts the probability that a coup will overthrow each of the world’s national leaders in each month.

  • The team gathered a proprietary training dataset by deducing likely drivers of coup attempts from academic research on coups dating back to 1920. In addition, it collected data detailing contemporaneous economic conditions such as gross domestic products, social conditions such as infant mortality, political conditions such as election schedules and regime longevity, and leader profiles such as age and military background.
  • The team trained two architectures, a random forest and an ensemble of regression models, to predict coup probabilities in logarithmic space, allowing a finer assessment of risk where coups are rare. They trained the regression models in an autoregressive fashion: First they trained a model on data between 1950 and 1974 to predict coup risks for 1975. They added the 1975 predictions to the dataset and retrained the model to predict risks for 1976, and so on to the present.
  • The two models are similarly good at predicting coups, but they're much more accurate when combined. The team combines their outputs using a generalized additive model.

Results: In 2021, the system predicted upheavals in Chad and Mali.
Behind the news: CoupCast is one of several efforts to use machine learning to study political tensions.

  • Atchai, a data science company, trained a transformer model on a dataset of global protests and political violence from Armed Conflict Location & Event Data (ACLED). The system analyzed news reports to determine the causes of protests, then used topic modeling and clustering to show how various protests relate to one another.
  • GroundTruth Global couples predictions drawn from machine learning with human analysis to understand volatility in developing economies.
  • The United States military developed a system that predicts whether actions such as arms sales or diplomatic visits will increase tensions between the U.S. and China.

Yes, but: The executive director of the nonprofit One Earth Future, which managed CoupCast from 2016 to 2021, came to doubt that its predictions could have a meaningful impact on policy, he told The Washington Post. This and other concerns prompted him to turn over the project to the University of Central Florida.

Why it matters: Technology that helps people see what’s on the horizon may help prevent coups from spiraling into civil wars and humanitarian crises — or at least help people prepare for the worst.

We’re thinking: Modeling political unrest is an important but challenging small-data problem; CoupCast’s dataset included only 600 positive examples. Given the extremely high stakes of international relations, a data-driven approach seems like a productive complement to human analysis.


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