Ethical AI and Librarianship
A Resource Guide
Humans in the Loop (HITL)
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Field
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Description |
|---|---|
| Title | Humans in the Loop (HITL) |
| Type | Projects & Institutions |
| Creator | Library of Congress Labs, AVP (metadata solution provider) |
| Link | https://labs.loc.gov/work/experiments/humans-loop/ |
| Creation Date | 09/2020 |
| Last Updated Date | 06/2021 |
| Summary | This project, led by the Library of Congress Labs team in collaboration with metadata solutions provider AVP, explored the integration of machine learning (ML) with human expertise to enhance access and discovery in digital library collections. Conducted from September 2020 to June 2021, the initiative addressed key challenges faced by cultural heritage institutions in using crowdsourcing endeavors to generate metadata, particularly for digital collections that are not suitable for OCR. The project produced a human-in-the-loop framework that combines ML with crowdsourcing, along with prototype workflows, training data samples, experimental user interfaces, and a final recommendations report. In particular, the final report emphasizes the need for: 1) Iterative testing and development of crowdsourcing platforms. 2) Exploration of other methods for sharing human-in-the-loop data, 3) Wider representation in the institutional to addressing risks and bias in machine learning workflows. This project offers a model for ethically applying machine learning in libraries and cultural heritage contexts by emphasizing the role of human judgment in machine learning processes. |
| Topic | AI and librarianship. Digital collection |
| Source and Link | Library of Congress Labs. https://labs.loc.gov/ |
| Access | Open |
| Accessibility | Open |
| Audience | Libraries – General. Information professionals. Scholars and Students. |
| Platform or Format | Web |
| Length | -- |
| Geography | USA |
| Language | ENG |
| Description Date | 06/04/2025 |