The project explores how machine learning, a form of automated data analysis, can help identify early signs that civic space is shifting and thereby help strengthen civil society’s resilience to potential shocks. To analyze civic space, INSPIRES is creating a database that combines locally created and curated data with existing digital data and longstanding research. The research findings will give stakeholders the tools they need to understand, assess, and apply machine learning to their work.

INSPIRES is piloting programs that strengthen the ability of civil society organizations to prevent or respond to threats to civic space. Building on our successful Legal Enabling Environment Program, ICNL leads the consortium that is assessing the legal aspects of those threats. Our activities include analyzing proposed laws and policies; providing expert advice on the design of strategies and responses; building coalitions and conducting awareness-raising campaigns; and organizing  convenings and capacity building. The project also provides advocates with valuable information about emerging and ongoing threats to enabling legal environments through our Civic Freedom Monitor.

INSPIRES’ robust learning agenda is building evidence-based strategies that can help human rights defenders and other civil society activists working in restricted civic spaces. By measuring progress against key indicators, the project provides actionable insights for protecting and enhancing civic space.