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Artificial intelligence (AI) now forms a part of various activities in the academic world. AI will also affect how research libraries perform and carry out their services and how the various kinds of data they hold in their repositories will be used in the future. For the moment, the landscape is complex and unclear, and library personnel and leaders are uncertain about where they should lay the path ahead. This extensive literature review provides an overview of how research libraries understand, react to, and work with AI. This paper examines the roles conceived for libraries and librarians, their users, and AI. Finally, design thinking is presented as an approach to solving emerging issues with AI and opening up opportunities for this technology at a more strategic level. [ABSTRACT FROM AUTHOR]

Academic search literacy and searches powered by artificial intelligence are a focus of the Royal Library and affiliated university libraries in Denmark. The ambition is to integrate AI-search tools in teaching and services that support literature seeking and hence improve the efficiency of the academic search process. However, before doing so, the library managers needed to learn more about the value AI-powered search tools have for information specialists and library users, and hence make informed decisions regarding investment in such tools. This paper presents a case study of two AI-search tools, which were tested via Think-aloud tests, a hackathon and an expert quality assessment at the Royal Library, Denmark. The results point to both opportunities and barriers for the implementation of AI-search tools at the library and we explore the consequences the results of the tests can have for library users and library services. In conclusion, there is a need for more research on the value of AI-search tools for information special)ists and library users. AI-search tools are continuously being developed and improved. The library needs to provide a critical approach to where in the search process the tools add value. Accordingly, the library needs to develop guidance on how to use AI-search tools as a supplement to more traditional approaches, how to report the use of the tools as part of an academic study and address the limitations of the tools. [ABSTRACT FROM AUTHOR]

Subject indexing, i.e., the enrichment of metadata records for textual resources with descriptors from a controlled vocabulary, is one of the core activities of libraries. Due to the proliferation of digital documents, it is no longer possible to annotate every single document intellectually, which is why we need to explore the potentials of automation on every level. At ZBW the efforts to partially or completely automate the subject indexing process started as early as 2000 with experiments involving external partners and commercial software. The conclusion of that first exploratory period was that commercial, supposedly shelf-ready solutions would not suffice to cover the requirements of the library. In 2014 the decision was made to start doing the necessary applied research in-house which was successfully implemented by establishing a PhD position. However, the prototypical machine learning solutions that they developed over the following years were yet to be integrated into productive operations at the library. Therefore in 2020 an additional position for a software engineer was established and a pilot phase was initiated (planned to last until 2024) with the goal to complete the transfer of our solutions into practice by building a suitable software architecture that allows for real-time subject indexing with our trained models and the integration thereof into the other metadata workflows at ZBW. In this paper we address the question of how to transfer results from applied research into a productive service, and we report on the milestones we have reached so far and on those that are yet to be reached on an operational level. We also discuss the challenges we were facing on a strategic level, the measures and resources (computing power, software, personnel) that were needed in order to be able to affect the transfer, and those that will be necessary in order to subsequently ensure the continued availability of the architecture and to enable a continuous development during running operations. We conclude that there are still no shelf-ready open source systems for the automation of subject indexing – existing software has to be adapted and maintained continuously which requires various forms of expertise. However, the task of automation is here to stay, and librarians are witnessing the dawn of a new era where subject indexing is done at least in part by machines, and the respective roles of machines and human experts may shift even further and more rapidly in a not-so-distant future. We argue that in general, the format of “project” and the mindset that goes with it may not suffice to secure the commitment that an institution and its decision-makers and the library community as a whole will have to bring to the table in order to face the monumental task of the digital transformation and automation in the long run. We also highlight the importance of all parties – applied researchers, software engineers, stakeholders – staying involved and continuously communicating requirements and issues back and forth in order to successfully create and establish a productive service that is suitable and equipped for operation. [ABSTRACT FROM AUTHOR]

The EU has proposed harmonized rules on artificial intelligence (AI Act) and a directive on adapting non-contractual civil liability rules to AI (AI liability directive) due to increased demand for trustworthy AI. However, the concept of trustworthy AI is unspecific, covering various desired characteristics such as safety, transparency, and accountability. Trustworthiness requires a specific contextual setting that involves human interaction with AI technology, and simply involving humans in decision processes does not guarantee trustworthy outcomes. In this paper, the authors argue for an informed notion of what is meant for a system to be trustworthy and examine the concept of trust, highlighting its reliance on a specific relationship between humans that cannot be strictly transmuted into a relationship between humans and machines. They outline a trust-based model for a cooperative approach to AI and provide an example of what that might look like. [ABSTRACT FROM AUTHOR]

This study primarily aims to identify unique theories and specific uses of theories in the library and information science (LIS) domain. It provides a comprehensive list of the theories used in LIS journal articles indexed by Scopus (an abstract and citation database) from 1970–2021. It expands on the most common theories and highlights the areas and purposes for which used theories in the LIS domain. Our goal is to demonstrate the usages and applications of various borrowed theories from complementary disciplines in the LIS domain. A systematical methodology is applied, following a few open-source AI-based software packages (such as ASReview, and OpenRefine), to analyse the theories against different parameters, keeping in mind the drawbacks of the previous studies. The study’s findings show that the LIS domain’s theoretical foundations are understudied. Researchers mainly borrowed theories from social sciences such as sociology, psychology, and management studies to solidify their domain. The paper provides a clear road map for the theoretical development of LIS research. And the resulting outputs may help policymakers, academicians, and researchers, irrespective of disciplines in general and information science in particular, understand the foundations and theoretical and methodological trends of theories that may lead to a better understanding of the theories before their selection and applications. [ABSTRACT FROM AUTHOR]

Subject indexing, i.e., the enrichment of metadata records for textual resources with descriptors from a controlled vocabulary, is one of the core activities of libraries. Due to the proliferation of digital documents, it is no longer possible to annotate every single document intellectually, which is why we need to explore the potentials of automation on every level. At ZBW the efforts to partially or completely automate the subject indexing process started as early as 2000 with experiments involving external partners and commercial software. The conclusion of that first exploratory period was that commercial, supposedly shelf-ready solutions would not suffice to cover the requirements of the library. In 2014 the decision was made to start doing the necessary applied research in-house which was successfully implemented by establishing a PhD position. However, the prototypical machine learning solutions that they developed over the following years were yet to be integrated into productive operations at the library. Therefore in 2020 an additional position for a software engineer was established and a pilot phase was initiated (planned to last until 2024) with the goal to complete the transfer of our solutions into practice by building a suitable software architecture that allows for real-time subject indexing with our trained models and the integration thereof into the other metadata workflows at ZBW. In this paper we address the question of how to transfer results from applied research into a productive service, and we report on the milestones we have reached so far and on those that are yet to be reached on an operational level. We also discuss the challenges we were facing on a strategic level, the measures and resources (computing power, software, personnel) that were needed in order to be able to affect the transfer, and those that will be necessary in order to subsequently ensure the continued availability of the architecture and to enable a continuous development during running operations. We conclude that there are still no shelf-ready open source systems for the automation of subject indexing – existing software has to be adapted and maintained continuously which requires various forms of expertise. However, the task of automation is here to stay, and librarians are witnessing the dawn of a new era where subject indexing is done at least in part by machines, and the respective roles of machines and human experts may shift even further and more rapidly in a not-so-distant future. We argue that in general, the format of “project” and the mindset that goes with it may not suffice to secure the commitment that an institution and its decision-makers and the library community as a whole will have to bring to the table in order to face the monumental task of the digital transformation and automation in the long run. We also highlight the importance of all parties – applied researchers, software engineers, stakeholders – staying involved and continuously communicating requirements and issues back and forth in order to successfully create and establish a productive service that is suitable and equipped for operation.

The EU has proposed harmonized rules on artificial intelligence (AI Act) and a directive on adapting non-contractual civil liability rules to AI (AI liability directive) due to increased demand for trustworthy AI. However, the concept of trustworthy AI is unspecific, covering various desired characteristics such as safety, transparency, and accountability. Trustworthiness requires a specific contextual setting that involves human interaction with AI technology, and simply involving humans in decision processes does not guarantee trustworthy outcomes. In this paper, the authors argue for an informed notion of what is meant for a system to be trustworthy and examine the concept of trust, highlighting its reliance on a specific relationship between humans that cannot be strictly transmuted into a relationship between humans and machines. They outline a trust-based model for a cooperative approach to AI and provide an example of what that might look like.

Academic search literacy and artificial intelligence (AI) search are a strategic focus of future researcher support strategies at the Royal Library and affiliated university libraries in Denmark. The ambition is to integrate AI search tools in teaching and services that support literature seeking and hence improve the efficiency of the academic search process. This paper is a reflection on the results of tests of AI powered search tools conducted at the Royal Library, Denmark. Information specialists and researchers took part in think-aloud tests, Hackathon and expert quality assessment in the period of April 2021 – February 2022. Our observations point to a) opportunities in AI search to let go of traditional approaches to search and broaden our perception of how an academic search can be conducted, b) potential of AI search interfaces to challenge ones preconception and reduce cognitive bias in the search process, c) concerns about the quality of scientific literature AI search identifies, and d) barriers that need to be overcome before practical implementation of Ai search tools become viable in academic searches. More research is needed to show the usefulness of AI search tools in systematic search and guidance on how to report the use of them

This document is a comment on an article discussing the use of AI systems in the judicial domain. It explores the potential risks and benefits of implementing AI in the legal system, emphasizing the importance of human control and striking a balance between human autonomy and legal automation. The document also addresses the limits of technology and the challenges of using AI for decision-making in the legal field. It mentions the debate surrounding the use of smart robots in international humanitarian law and the differing opinions on fully automating lethal force. Overall, the comment provides insights into the complexities and implications of integrating AI into the legal system, highlighting the need for regulation and public deliberation. [Extracted from the article]

The Barcelona Historical Marriage Database (BHMD) gathers records of the more than 600,000 marriages celebrated in the Diocese of Barcelona and their taxation registered in Barcelona Cathedral's so-called Marriage Licenses Books for the long period 1451-1905 and the BALL Demographic Database brings together the individual information recorded in the population registers, censuses and fiscal censuses of the main municipalities of the county of Baix Llobregat (Barcelona). In this ongoing collection 263,786 individual observations have been assembled, dating from the period between 1828 and 1965 by December 2020. The two databases started as part of different interdisciplinary research projects at the crossroads of Historical Demography and Computer Vision. Their construction uses artificial intelligence and computer vision methods as Handwriting Recognition to reduce the time of execution. However, its current state still requires some human intervention which explains the implemented crowdsourcing and game sourcing experiences. Moreover, knowledge graph techniques have allowed the application of advanced record linkage to link the same individuals and families across time and space. Moreover, we will discuss the main research lines using both databases developed so far in historical demography. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) now forms a part of various activities in the academic world. AI will also affect how research libraries perform and carry out their services and how the various kinds of data they hold in their repositories will be used in the future. For the moment, the landscape is complex and unclear, and library personnel and leaders are uncertain about where they should lay the path ahead. This extensive literature review provides an overview of how research libraries understand, react to, and work with AI. This paper examines the roles conceived for libraries and librarians, their users, and AI. Finally, design thinking is presented as an approach to solving emerging issues with AI and opening up opportunities for this technology at a more strategic level.

The article discusses the use of artificial intelligence (AI) in the judicial system and explores the idea of AI regulating AI. It examines two ways in which AI can regulate AI: through building regulatory objectives into the software itself and through the use of debugging features. The article also considers the relationship between law and regulation and how AI regulating AI can ensure compliance with legal requirements, particularly in terms of fairness. While AI regulating AI may address some concerns about AI in the legal system, it does not fully address the broader concerns about the appropriate use of technology in the legal and political systems. [Extracted from the article]