PROGRAM

Carnegie Mellon University Libraries is currently developing an innovative suite of AI-powered tools addressing two critical challenges in research information management: the adoption barrier for public researcher profiles and the balanced distribution of committee service responsibilities among faculty. Our prototype toolset leverages natural language processing and machine learning techniques to extract committee service data from faculty CVs and other unstructured sources, import this information into the Elements/Scholars@CMU platform, and analyze the data to provide recommendations for achieving greater balance in committee service workload. This presentation will share our in-progress development approach, initial prototype functionality, methodological considerations, and preliminary findings. We'll demonstrate how libraries can position themselves as essential curators of institutional data while advancing fairness initiatives through innovative applications of AI in research information management.