For at least the last two decades, there have been calls within the United Nations to develop robust, accurate, and effective early warning systems for conflict prevention. Indeed, as recently as September 2011, Secretary-General Ban Ki-moon and the UN Security Council reiterated this need in their report “Preventative Diplomacy: Delivering Results.” The president of the Security Council at the time stated that a “key component…of a comprehensive conflict prevention strategy include[s] early warning [mechanisms].” The need for comprehensive early warning systems to analyze and disseminate data on sociopolitical and armed conflict dynamics within the UN system is well established.
Yet one of the main operational challenges to early warning is clear: how to aggregate incoming information and data to derive actionable intelligence on an emerging situation. Often (but not always), incoming data is highly qualitative, which can place strains on the limited capacity of international organizations (IOs) and non-governmental organizations (NGOs). In addition, quantitative data is often not collected in a way that can easily be fed into a larger system. Organizations can find it too resource-intensive to clean, process, and analyze the data, thus limiting the type and volume of data being looked at.
One way to overcome these resource constraints is to create tools that can automate the processing and analysis of quantitative data. Machine learning and data science seems a natural fit to improve this process. Data science is a multidisciplinary field that applies a mix of mathematics, statistics, computer science, data modeling and visualization, graphic design and hacking, as well as specific subject area expertise. Machine learning is a branch of computer science that leverages algorithms, or a set of step-by-step computer procedures, to perform actions without explicitly being programmed to. Machine learning has been used by a wide variety of private sector organizations for things like targeting user recommendations, detecting fraud and identity theft, and ad optimization.
Automated early warning systems can help NGOs and IOs in a number of ways. They can help organizations develop an evidence base to create the political will to do preventative work to intervene or mitigate negative effects of large-scale conflict as tensions ramp up. In the case of predicting conflict, organizations can use early warning risk assessments for better planning and try to target non-conflict interventions that have conflict-mitigating knock-on effects in high-risk areas.