(NewsUSA) - Part of the mission of intelligence communities (ICs) is to alert leaders to the likelihood and implications of events such as imminent acts of military aggression by adversaries, sudden political transitions, or economic crises. Policy makers need as much warning as possible in order to prepare next moves in response to potential disruptions or threats on a global scale.

“As the world becomes more and more digitized, the volume of openly-available information is exploding, making it more difficult for analysts to quickly sort through mountains of classified and unclassified information to determine which indicators actually matter, which data to believe, and which to treat with skepticism,” according to experts at the Special Competitive Studies Project (SCSP), a nonprofit and nonpartisan initiative with a goal of making recommendations to strengthen America's long-term competitiveness in AI.

However, artificial intelligence can help analysts improve their strategic warning systems. SCSP noted several ways in which the intelligence community could leverage the power of AI for early warnings:

- Open-source monitoring. The next generation of AI tools could monitor the open-source information space of countries where strategic events are currently not expected, which would allow the IC to deploy human analysts where they are most needed, and reallocate analysts as needed if AI warns of anomalous activity. 

- Scenario generation. All-source intelligence analysts are often assessing a range of possible events and outcomes. When analysts manually generate these scenarios, they are limited by time and to what they already believe to be likely outcomes. Instead, scenario generation by AI could help analysts quickly identify new scenarios they had not yet considered and weigh those scenarios against one another to determine the most likely outcomes. 

- Data fusion and synthesization. An AI system that could process various types of intelligence, such as integrating public commentary and speeches by political leaders to detect patterns would help U.S. policy makers get inside the heads of world leaders and inform decision-making.

- Improvements in human-machine teaming. Use of tools such as crowdsourced human forecasts and automatic human feedback would combine the breadth of expertise of human analysts and the speed of AI; humans would be in key positions in the process, including the development of assessments where human-based explainability is essential.

Even long-term, a human analyst will likely need to validate an AI’s system outputs before passing them to policymakers. However, there is no downside to that because it will allow for consistent fine-tuning, experimentation, and improvement with the goal of generating the quickest, most reliable warnings for decision makers, according to SCSP.

Visit scsp.ai to learn more.