Artificial intelligence (AI) has the potential to positively impact library and archives collections and services—enhancing reference, instruction, metadata creation, recommendations, and more. However, AI also has ethical implications. This paper presents an extensive literature and review analysis that examines AI projects implemented in library and archives settings, asking the following research questions: RQ1: How is artificial intelligence being used in libraries and archives practice? RQ2: What ethical concerns are being identified and addressed during AI implementation in libraries and archives? The results of this literature review show that AI implementation is growing in libraries and archives and that practitioners are using AI for increasingly varied purposes. We found that AI implementation was most common in large, academic libraries. Materials used in AI projects usually involved digitized and born digital text and images, though materials also ranged to include web archives, electronic theses and dissertations (ETDs), and maps. AI was most often used for metadata extraction and reference and research services. Just over half of the papers included in the literature review mentioned ethics or values related issues in their discussions of AI implementation in libraries and archives, and only one-third of all resources discussed ethical issues beyond technical issues of accuracy and human-in-the-loop. Case studies relating to AI in libraries and archives are on the rise, and we expect subsequent discussions of relevant ethics and values to follow suit, particularly growing in the areas of cost considerations, transparency, reliability, policy and guidelines, bias, social justice, user communities, privacy, consent, accessibility, and access. As AI comes into more common usage, it will benefit the library and archives professions to not only consider ethics when implementing local projects, but to publicly discuss these ethical considerations in shared documentation and publications.

This study presents findings with a particular digital library system, the Belgian‐American Research Collection at the University of Wisconsin Digital Collections Center (UWDCC). It focuses on the ways in which a usability study can inform the future design of the collection and other UWDCC projects. The study uses two formal testing instruments: a focus group and a task‐oriented field test. Both of these techniques yielded solid data about user expectations, problems and actions with the collection. While the findings can aid UWDCC designers as they begin to envision another iteration of the Belgian‐American Research Collection, the findings also point to a need for a shift in the development of more complete digital library systems. This study proposes the development of digital library systems that have a strong orientation toward users and usability as well as advanced search interfaces and full metadata. In the end, it is not just about cataloging and organizing information; it is also about presenting information logically and intuitively for your users.

Today's learners operate in digital environments which can be largely navigated with no human intervention. At the same time, libraries spend millions and millions of dollars to provide access to content which our users may never know is available to them. Through the Open SESMO (Search Engine & Social Media Optimization) database project, Montana State University (MSU) Library applied search engine optimization and structured data with the Schema.org vocabulary, linked data models and practices, and social media optimization techniques to all the library's subscribed databases. Our research shows that Open SESMO creates significant return-on-investment with substantial increased traffic to our paid resources by our users as evidenced through analytics and metrics. In the core research of the article, we take a quantitative look at the pre/post results to assess the Open SESMO method and its impact on organic search referrals and use of the collection analyzing data from three distinct fall semesters. Returns include demonstrated library value through database recommendations, connecting researchers to subject librarians, and increased visitation to our library's paid databases with growth in organic search referrals, impressions, and click-through rates. This project offers a standard and innovative practice for other libraries to employ in surfacing their paid databases to users through the open web by applying structured and linked data methods.

Libraries expand the access and visibility of data and research in support of an informed public. Search engines have limited knowledge of the dynamic nature of libraries - their people, their services, and their resources. The very definition of libraries in online environments is outdated and misleading. This article offers a solution to this metadata problem by redefining libraries for Machine Learning environments and search engines. Two ways to approach this problem include implementing local structured data in a knowledge graph model and “inside-out” definitions in Semantic Web endpoints. MSU Library has found that implementing a “Knowledge Graph” linked data model leads to improved discovery and interpretation by the bots and search engines that index and describe what libraries are, what they do, and their scholarly content. In contrast, LSE Library has found that contributing to Wikidata, a collaborative and global metadata source, can increase understanding of libraries and extend their reach and engagement. This article demonstrates that Wikidata can be used to push out data, the technical details of knowledge graph markup, and the practice of semantic Search Engine Optimization (SEO). It explores how metadata can represent an organization equitably and how this improves the reach of global information communities.