AI for the Processing of Audio Records

This 3-hour workshop is based on an Open Educational Resource developed by the researchers on uses of Artificial Intelligence (AI) for the processing of digitized Audio Records in the context of the InterPARES Trust AI Project. It introduces a framework for AI applications to the processing of audio records, case studies, critical assessments of the limitations of using AI in this context, and active hands-on exercises. Attendees will engage in discussions, interact with AI tools on digitized audio-records, and share their own experiences and concerns regarding the use of AI for the processing of audio records in archives or records management offices. After this workshop, attendees will be able to: 1) discuss potential uses of AI in the life cycle of audio records; 2) use AI tools to process digitized audio records; 3) explain AI pipelines for the processing of audio records; and 4) critically identify limitations of AI for the processing of audio records and their incorporation into archival workflows.

Nataliya L. Radke
I completed the MASLIS program at the School of Information at UBC in 2025. Previously, I worked on the InterPARES Trust AI project (Teachable AI for the Archival Professions, 2021-2026) and have since joined the University Archives at UBC as a Processing Archivist. My current research interests include practical applications for artificial intelligence in archives and how machine learning tools can empower archivists to do more with less while improving patron access to materials.

Peter Sullivan

Dr. Richard Arias-Hernández
I am an Associate Professor of Teaching at the School of Information at UBC and Academic Director at UBC Vantage College. I teach technology-related courses in the Mater of Archival Studies at UBC. I am currently the PI of the InterPARES Trust AI project Teachable AI for the Archival Professions (2021-2026). This research focuses on curriculum design for archival studies programs to leverage and incorporate artificial intelligence and machine learning into archival and records management practices.