Library of Congress: “Introducing the LC Labs Artificial Intelligence Planning Framework”

From The Library of Congress:

LC Labs has been exploring how to use emerging technologies to expand the use of digital materials since our launch in 2016. We quickly saw machine learning (ML), one branch of artificial intelligence (AI), as a potential way to provide more metadata and connections between collection items and users. Experiments and research have shown the risks and benefits of using AI in libraries, archives and museums (LAMs) are both significant yet still largely hypothetical. In short:

• Library collections are incredibly diverse and are particularly challenging for current machine learning and AI tools to process predictably.
• New AI tools with impressive claims are being released rapidly. We benefit from testing these tools openly, collaborating, and learning from others.
• AI-specific quality standards and policies that support our context of providing authoritative resources to the public over the long-term need to be developed and communicated to partners and vendors.
• While large-scale implementation of responsible AI in LAMs is still several years away, now is the time to increase experimentation and collaboration both within our organization and across the sector.

To account for these challenges and realities, LC Labs has been developing a planning framework to support the responsible exploration and potential adoption of AI at the Library. At a high level, the framework includes three planning phases:  1) Understand 2) Experiment and 3) Implement, each supports the evaluation of three elements of ML: 1) Data; 2) Models; and 3) People. We’ve developed a set of worksheets, questionnaires, and workshops to engage stakeholders and staff and identify priorities for future AI enhancements and services. The mechanisms, tools, collaborations, and artifacts together form the AI Planning Framework. Our hope in sharing the framework and associated tools in this initial version is to encourage others to try it out and to solicit additional feedback. We will continue updating and refining the framework as we learn more about the elements and phases of ML planning.

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Filed under: Archives and Special Collections, Companies (Publishers/Vendors), Data Files, Libraries, News, Patrons and Users