Ethical AI and Librarianship
A Resource Guide
Machine Learning + Libraries Summit Event Summary
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Field
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Description | |
|---|---|---|
| Title | Machine Learning + Libraries Summit Event Summary | |
| Type | Reports | |
| Creator | Eileen Jakeway, Lauren Algee, Laurie Allen, Meghan Ferriter, Jaime Mears, Abigail Potter, Kate Zwaard | |
| Link | https://labs.loc.gov/static/labs/meta/ML-Event-Summary-Final-2020-02-13.pdf | |
| Creation Date | 02/13/2020 | |
| Last Updated Date | -- | |
| Summary | Prepared by Library of Congress Labs and authored by Eileen Jakeway et al., this summary report covers the event “Machine Learning + Libraries Summit” hosted by the Library of Congress on September 20, 2019. The one-day event brought together 75 cultural heritage professionals from libraries, museums, and universities to explore the opportunities, challenges, and ethical implications of applying machine learning (ML) in library and cultural heritage settings. The report summarizes key discussion themes and outputs from group activities. Emerging threads from the discussions include: 1) Ethical issues in ML, such as bias in training datasets, the need for transparency and communication to mitigate such bias, and the ethical implications of human labor in commercial ML systems. 2) Barriers to access, including limited computing resources and technical expertise. 3) Challenges in attracting practitioners to work with GLAM (galleries, libraries, archives, museums) datasets. 4) The importance of engaging researchers in the humanities and social sciences who may not yet recognize the value of ML for their work. 5) Challenges of the operationalization of ML in daily practices, which requires institutional support and infrastructure. 6) The use of crowdsourcing for ML training data, along with ethical concerns such as volunteer fatigue. 7) The need for community-developed metrics to evaluate ML projects before, during and after implementation; 8) Copyright concerns related to using large datasets for ML applications. The summary also presents machine learning-related milestones proposed by participants, some of which address ethical issues. These include collecting ethical considerations for applying AI/ML in cultural heritage institutions, developing guidelines to reduce dataset bias, implementing data standards for released datasets and collaborating with data creators to build ethical models and taxonomies. | |
| Topic | Ethical AI. AI and Librarianship. | |
| Source and Link | Library of Congress Labs. https://labs.loc.gov/ | |
| Access | Open | |
| Accessibility | -- | |
| Audience | Librarians – General. Scholars and Students. Information Professionals. | |
| Platform or Format | Document (.pdf) | |
| Length | 39 pages | |
| Geography | USA | |
| Language | ENG | |
| Description Date | 06/18/2025 |