• Read more about Data and methods for identifying artificial intelligence-related patents

This paper evaluates existing approaches to identifying artificial intelligence (AI)-related patents and introduces a novel, scalable framework to improve classification performance. Motivated by the growing reliance on patent data in innovation research, we assess widely used methods and document their substantial performance limitations. To address these challenges, we develop a CPC-informed label-refinement framework inspired by positive-unlabeled (PU) learning to construct high-quality training data.

• Read more about From Hotspots to Help-Spots: An AI-enabled, Community-centered Risk-resource Mapping

This project will focus on Baltimore City and/or Prince George’s County, with the final study site selected in consultation with researchers and community partners. The project’s central contribution will be the integration of two complementary geospatial frameworks: risk terrain and resource terrain.