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AI at LC

three men standing in a room of computers
From left to right: George R. Perreault, head of the Library of Congress Data Processing Offiice, standing at the computer storage unit; Ernest Acosta Jr., digital computer programmer, working at the card reader unit; and Joseph B. Murphy, digital computer programmer, inserting a new tape in one of the tape units. Jan. 20, 1964. Item 1333, box 69, Photographs, Illustrations & Objects, Library of Congress Archives, Manuscript Division, Library of Congress, Washington, D.C. Read more about the history of computing at the Library in this blog post.

Experimenting with artificial intelligence and machine learning at the Library of Congress.

Since 2018, the Library of Congress has been researching and experimenting with artificial intelligence and machine learning, focusing on ethical uses of these technologies, and addressing the challenges of their adoption in libraries and cultural memory organizations.

Below are resources from expert consultations, explorations with Library staff and users, and experiments demonstrating how automated technology can enhance collections, operations, and services.

Worldwide Engagement

The Library of Congress has been engaging in international, federal, and sector-wide community conversations about AI for years, including participation in AI conferences as part of the International Federation of Library Associations (IFLA), and as a member of the Secretariat of the international AI4LAMs community alongside other National Libraries and major research institutions.

Within the federal community, Library staff have joined federal communities of practice, including the Equitable Data Community of Practice, the Congressional AI Advisory Group, and the AI Community of Practice hosted by GSA, including a subgroup chaired by a Library staff member dedicated to developing requirements for vendors to adhere to when using Natural Language Processing (NLP).

Active Artifical Intelligence Use Cases

Use cases for artificial intelligence at varying stages include:

  • Creating machine-readable text from digitized documents using Optical Character Recognition (OCR) to support search and discovery of collections and content online.
  • Creating standardized catalog records from eBooks and other digital material- testing different machine learning (ML) models to generate data for bibliographic records, measuring the quality of outcomes, and understanding the use of ML in the cataloging processing.
  • Extracting key data from historic, handwritten and typed Copyright records forms - experiment to train multiple ML models with historical legislative data to generate terms for geographic and organizational data fields from established vocabularies, for the enhanced discovery and management of legislative data in
  • Summarizing Bill text - experiment to train a ML model with legislative bill data to generate bill summaries using Natural Language Processing (NLP), with an emphasis on measuring the quality of outcomes and analyzing the use of ML in the larger bill summary workflow with analysts to maintain the highest standard of quality and accuracy.

Experiments to Date

Reports and Presentations

Artificial Intelligence Governance

Our current policies guide the use of technology to meet the agency’s mission, encouraging the adoption of tools and technology that will improve our ability to meet the information needs of Congress and the American people. As experiments with AI move to additional planning and implementation, policies and governance frameworks will be updated to reflect the particular challenges and opportunities presented by these tools. This includes Library policy reviews and updates and new policy evaluations.

Over the past several years, we have been developing a framework for AI decision making that aligns closely with the AI Risk Management Framework from the National Institute for Standards and Technology (NIST) and recommendations of the Office of Management and Budget in Memorandum M-21-06 titled "Guidance for Regulation of Artificial Intelligence Applications" to work towards voluntary sector-based standards, to make data available, and to communicate with the public.

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