Machine Learning for Peace

"The Machine Learning for Peace Project is seeking to understand how civic space is changing in countries across the world. Working with partners in the INSPIRES consortium, we identify important shifts in civic space in real time using state of the art machine learning techniques. Using the latest innovations in natural language processing, we classify an enormous corpus of digital news into 19 types of civic space "events" and 22 types of Resurgent Authoritarian Influence (RAI) “events” which capture the efforts of authoritarian regimes to wield influence on developing countries.

We combine our civic space event data with high frequency economic data to identify key drivers of civic space and forecast shifts in coming months. Ultimately, we hope our approach will be a useful tool for researchers seeking rich, high-frequency data on political regimes and for policymakers and activists fighting to defend democracy around the world."

Machine Learning for Peace comprised the key components of Civic Space they are tracking; defined as an action that affects civic space openness.

The Enabling and Protecting Civic Space (EPCS) Illuminating New Solutions and Programmatic Innovations for Resilient Spaces (INSPIRES) project is seeking to understand how civic space is changing in countries across the world and to predict when future changes are likely to occur. INSPIRES is testing how machine learning can help predict shocks to civic space before they occur in order to provide time-sensitive insights that might aid citizens, civil society organizations, and the media in strategically addressing emerging threats.

Watch the launch here.

Machine Learning for Peace