Feasible, Adaptable and Shared: A call for a community framework for implementing ML and AI
Abstract
Through open research, experimentation and convenings with LAM sector peers and colleagues, a foundational need has emerged for a broadly shared and evidenced set of guidelines for implementing ML and AI technologies that centers the long-term stewardship and ethical responsibilities of cultural heritage organizations. Inspired by community guidelines that rationalize complex information into an understandable framework like the NDSA Level of Digital Preservation and the Data Nutrition Project, LC Labs is proposing a step toward collaboratively generating a LAM-specific framework for understanding and implementing ML and AI technologies.Details
- Creators
- Abigail Potter; Meghan Ferriter; Eileen Manchester; Jaime Mears
- Institutions
- Library of Congress
- Date
- 2022-09-16 00:00:00 +0000
- Keywords
- community guidelines
- Publication Type
- long paper
- License
- CC-BY 4.0 International
- Download
- (unknown) bytes
- Slides
- here
- Video Stream
- here
- Collaborative Notes
- here