Policy issues surrounding the use of geospatial information are examined in two new reports from the Congressional Research Service.
“Geospatial information is data referenced to a place–a set of geographic coordinates–which can often be gathered, manipulated, and displayed in real time. A Geographic Information System (GIS) is a computer data system capable of capturing, storing, analyzing, and displaying geographically referenced information.”
“The federal government and policy makers increasingly use geospatial information and tools like GIS for producing floodplain maps, conducting the census, mapping foreclosures, congressional redistricting, and responding to natural hazards such as wildfires, earthquakes, and tsunamis. For policy makers, this type of analysis can greatly assist in clarifying complex problems that may involve local, state, and federal government, and affect businesses, residential areas, and federal installations.”
See “Geospatial Information and Geographic Information Systems (GIS): An Overview for Congress” (pdf), May 18, 2011, and “Issues and Challenges for Federal Geospatial Information” (pdf), May 18, 2011.
After months of delay, the council tasked by President Trump to review the FEMA released its final report. Our disaster policy nerds have thoughts.
FAS and FLI partnered to build a series of convenings and reports across the intersections of artificial intelligence (AI) with biosecurity, cybersecurity, nuclear command and control, military integration, and frontier AI governance. This project brought together leaders across these areas and created a space that was rigorous, transpartisan, and solutions-oriented to approach how we should think about how AI is rapidly changing global risks.
Investment should instead be directed at sectors where American technology and innovation exist but the infrastructure to commercialize them domestically does not—and where the national security case is clear.
AI is already consequential, but its future trajectory remains contested. Policymakers should make their assumptions explicit, focus on what can be shaped rather than what can be perfectly predicted, and build institutions that can learn and respond as evidence changes.