To address shrinking civic space in a world of growing authoritarianism, the United States Agency of International Development (USAID) launched the Illuminating New Solutions and Programmatic Innovations for Resilient Spaces (INSPIRES) website on December 15. The site highlights critical user-friendly data that has been sourced from machine learning -- or a set of methods for training computers to detect patterns or structures in data -- and will be advantageous to citizens, civil society, media and others to get ahead of harmful repressive trends on closing space. This historic and unprecedented data repository, shared in easily digestible visual formats, will set the precedent for how machine learning can safely monitor the status of democracy, human rights, and governance.
With funding from USAID’s Center for Democracy, Human Rights, and Governance, implementing partner Internews is leading a seven-member consortium, including Duke University, Partners Global, Results for Development, the International Center for Not-for-Profit Law, CIVICUS, and Zinc Network, to implement the Enabling and Protecting Civic Space INSPIRES project. Last year, through analyzing over 90 million news articles within this project, Duke University proved that machine learning can predict the closing of civic space in a future time from one, three, and six months with statistically significant accuracy.
Through this activity and many others, INSPIRES is increasing the knowledge and capacity of citizens, organizations, the media, and donors, to quickly respond to growing restrictions on democratic freedoms of association, assembly, and expression. With civic space shifting rapidly around the world, faster and more potent technological interventions, like this one, are critical for bolstering local civil societies. Through in-depth research, earlier forecasting of civic space shifts, and rigorous evaluation of different interventions, civil society can better prepare to navigate these shifts through proactive, effective, and strategic action.