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The aim of the paper is to define Artificial Intelligence (AI) for librarians by examining general definitions of AI, analysing the umbrella of technologies that make up AI, defining types of use case by area of library operation, and then reflecting on the implications for the profession, including from an equality, diversity and inclusion perspective. The paper is a conceptual piece based on an exploratory literature review, targeting librarians interested in AI from a strategic rather than a technical perspective. Five distinct types of use cases of AI are identified for libraries, each with its own underlying drivers and barriers, and skills demands. They are applications in library back-end processes, in library services, through the creation of communities of data scientists, in data and AI literacy and in user management. Each of the different applications has its own drivers and barriers. It is hard to anticipate the impact on professional work but as information environment becomes more complex it is likely that librarians will continue to have a very important role, especially given AI's dependence on data. However, there could be some negative impacts on equality, diversity and inclusion if AI skills are not spread widely. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) and machine learning (ML) is increasingly becoming prevalent in libraries and information centres, promising to improve service delivery and efficiency. However, the use of these technologies also poses significant ethical challenges and risks, including bias and discrimination, privacy and security, automation and job displacement, and the lack of human interaction in service delivery. This conceptual paper provides an overview of the key ethical frameworks and principles relevant to the use of AI and ML in libraries and information centres, and analyses how these frameworks can be applied to this specific context. The paper also discusses the potential benefits and risks of these technologies, and provides best practices and strategies for mitigating the ethical risks associated with their use. Finally, the paper highlights the implications of these findings for libraries and information centres, and recommends future research and practice in this area, including on-going evaluation and monitoring of the ethical implications of AI and ML. Overall, this paper underscores the importance of taking a proactive and ethical approach to the use of AI and ML in libraries and information centres to ensure that these technologies are used in a way that aligns with their mission and values, and serves the best interests of their users and society as a whole. [ABSTRACT FROM AUTHOR]

We are already living in an algorithmic society. AI policies and regulations are now emerging at the same time as more is learned about the implications of bias in machine learning sets, the surveillance risks of smart cities and facial recognition, and automated decision-making by government, among many other applications of AI and machine learning. Each of these issues raises concerns around ethics, privacy, and data protection. This paper introduces some of the key AI regulatory developments to date and engagement by libraries in these processes. While many AI applications are largely emergent and hypothetical in libraries, some mature examples can be identified in research literature searching, language tools for textual analysis, and access to collection data. The paper presents a summary of how library activities such as these are represented in national AI plans and ways that libraries have engaged with other aspects of AI regulation including the development of ethical frameworks. Based on the sector's expertise in related regulatory issues including copyright and data protection, the paper suggests further opportunities to contribute to the future of ethical, trustworthy, and transparent AI. [ABSTRACT FROM AUTHOR]

Purpose: This paper examines the use of Artificial Intelligence/Machine Learning to extract a more comprehensive data set from a structured “standardized” form used to document Japanese American incarcerees during World War II.Setting/Participants/Resources: The Bancroft Library partnered with Densho, a community memory organization, and Doxie.AI to complete this work. Brief Description: The project digitized the complete set of Form WRA-26 “individual records'' for more than 110,000 Japanese Americans incarcerated in War Relocation Authority camps during WWII. The library utilized AI/machine learning to automate text extraction from over 220,000 images of a structured “standardized” form; our goal was to improve upon and collect information not previously recorded in the Japanese American Internee Data file held by the National Archives and Records Administration. The project team worked with technical, academic, legal, and community partners to address ethical and logistical issues raised by the data extraction process, and to assess appropriate access options for the dataset(s) and digitized records. Results/Outcome: Using AI/machine learning increased the quality of the data extracted from the digitized WWII era forms. Evaluation Method: A comparison of the earlier dataset extracted from the 1940s’s computer punch cards to the current data set extracted using AI/machine learning, the use of AI/machine learning showed marked improvement.

Applications of artificial intelligence (AI) and machine learning (ML) have become pervasive in our everyday lives. These applications range from the mundane (asking ChatGPT to write a thank you note) to high-end science (predicting future weather patterns in the face of climate change), but, because they rely on human-generated or mediated data, they also have the potential to perpetuate systemic oppression and racism. For museums and other cultural heritage institutions, there is great interest in automating the kinds of applications at which AI and ML can excel, for example, tasks in computer vision including image segmentation, object recognition (labelling or identifying objects in an image) and natural language processing (e.g. named-entity recognition, topic modelling, generation of word and sentence embeddings) in order to make digital collections and archives discoverable, searchable and appropriately tagged. A coalition of staff, Fellows and interns working in digital spaces at the Smithsonian Institution, who are either engaged with research using AI or ML tools or working closely with digital data in other ways, came together to discuss the promise and potential perils of applying AI and ML at scale and this work results from those conversations. Here, we present the process that has led to the development of an AI Values Statement and an implementation plan, including the release of datasets with accompanying documentation to enable these data to be used with improved context and reproducibility (dataset cards). We plan to continue releasing dataset cards and for AI and ML applications, model cards, in order to enable informed usage of Smithsonian data and research products. [ABSTRACT FROM AUTHOR]

In recent years, cultural heritage institutions have been exploring the benefits of applying Linked Open Data to their catalogs and digital materials. Innovative and creative methods have emerged to publish and reuse digital contents to promote computational access, such as the concepts of Labs and Collections as Data. Data quality has become a requirement for researchers and training methods based on artificial intelligence and machine learning. This article explores how the quality of Linked Open Data made available by cultural heritage institutions can be automatically assessed. The results obtained can be useful for other institutions who wish to publish and assess their collections. [ABSTRACT FROM AUTHOR]

The field of explainable artificial intelligence (XAI) advances techniques, processes, and strategies that provide explanations for the predictions, recommendations, and decisions of opaque and complex machine learning systems. Increasingly academic libraries are providing library users with systems, services, and collections created and delivered by machine learning. Academic libraries should adopt XAI as a tool set to verify and validate these resources, and advocate for public policy regarding XAI that serves libraries, the academy, and the public interest., INTRODUCTION Explainable artificial intelligence (XAI) is a subfield of artificial intelligence (AI) that provides explanations for the predictions, recommendations, and decisions of intelligent systems. (1) Machine learning is rapidly becoming [...]