Day 1 (May 28)
All times are US Eastern. Click on time to find the time in your local time zone.
This panel will provide a comprehensive introduction to artificial intelligence, demystifying the concept and clarifying its various dimensions. Attendees will learn about the history of AI, key terminologies, and the different types of AI systems, including narrow AI, general AI, and superintelligent AI. By exploring real-world applications and potential future developments, participants will gain a clear understanding of what AI is, what it can do, and how it is shaping our world.
Instructors:
Andrew Cox
Souvick Ghosh
Darra Hoffman
Lingzhi Hong
AI is likely to significantly impact libraries both directly through uses by the library and indirectly through how it changes user behavior. As a result, the range and depth of impacts is hard to fully understand, but it is likely to affect all library operations and library roles. The session seeks to describe some of this context. It will also explore the drivers of change and the challenges, including of defining responsible AI in the library context.
Horizon scanning to discuss how AI might be evolving over the next period and how to monitor such changes.
Learning Objectives:
- To appreciate the range of library uses of AI and indirect impacts
- To evaluate potential uses of AI in the participant’s context, taking into account benefits and challenges
- To understand how to effectively monitor the changing scene and its implications
Instructor:
Andrew Cox
This session will provide library practitioners with a hands-on introduction to the interdisciplinary field of Human-Computer Interaction (HCI). Through interactive activities during the session, participants will explore HCI methodologies and understand their relevance to library services. The session will focus on the relationship between HCI and other domains, such as computer science, cognitive science, and psychology. Participants will gain insights into how these disciplines intersect with library practices through the hands-on exercises. Practical exercises will focus on topics such as interface design, implementation, and evaluation. At the conclusion of the session, participants will have the opportunity to showcase their hands-on work.
Learning Objectives:
- Understand the interdisciplinary nature of Human-Computer Interaction (HCI) and its relevance to library practitioners.
- Explore various HCI methodologies and their applications in library settings.
- Acquire practical experience in interface design, implementation, and evaluation, with a focus on addressing challenges faced by library practitioners.
Instructors:
Nada Atar
Souvic Ghosh
This session will cover the fundamental concepts and techniques of machine learning (ML). Attendees will learn about the different types of ML, including supervised and unsupervised, and the algorithms associated with each type. The session will introduce key concepts such as training data, model fitting, and prediction. Participants will also explore practical applications of ML – both code and no-code approaches – using real-world datasets. This course aims to provide a solid foundation in ML, equipping attendees with the knowledge to understand and leverage ML technologies in their respective fields.
Learning Objectives:
Upon successful completion of this course, students will be able to:
- Understand the theory behind various machine learning algorithms and their application to real-world data problems.
- Learn to develop machine learning algorithms with or without coding
- Analyze and compare machine learning tools and algorithms and apply them suitably for solving problems.
- Create simple visualizations to explore and analyze data
Souvick Ghosh
Day 2 (May 29)
All times are US Eastern. Click on time to find the time in your local time zone.
The internet offers a vast amount of data, necessitating an understanding of how to utilize this wealth of information more effectively. This knowledge will not only serve to advance library related research, e.g., digital resource management, data-driven library services, but also equip librarians the knowledge and skills to provide data literacy services that assist users in better comprehending and employing online data. This session will cover data collection, classification, and transformation methods using Python. For data collection, we will provide an overview of web communication through hyperlinks and APIs. Practical considerations for accessing APIs, such as obtaining credentials and making requests using Python. For data classification and transformation, we will introduce the use of open sourced large language models (LLM) such as Llama2 for the automatic classification of data into predetermined categories or hierarchies for further analysis. By the end of the course, participants will be equipped with the necessary knowledge and skills to collect, preprocess, and analyze data from online platforms, thereby enhancing their ability to conduct research and provide data related services.
Learning Objectives:
- Obtain an understanding of data acquisition, an important component of data literacy competency.
- Understand the basics of web data and APIs.
- Acquire practical skills in using programming languages such as Python and its libraries to scrap data from webpages and APIs.
- Obtain practical skills in using open sourced LLMs for automatic processing of large-scale data.
- Integrate the tutorial material into their data literacy education services.
- Encourage innovative thinking about how online data can support library research, and the development of new tools and services.
Instructor:
Dr. Lingzi Hong
Xiaoying Song
This presentation will explore key trends and issues related to the emergence of generative artificial intelligence in libraries and archives. This session will primarily focus on two core topics: 1. AI literacy and prompt engineering, and how these are competencies that can fall squarely within the domain and expertise of librarians, giving them a new role through which to demonstrate their value as professionals; 2. ethical issues surrounding the emerging usage of artificial intelligence on college campuses and cultural heritage institutions, including research related to the prevalence and impact of AI-Presenters:
Brady Lund
Patricia Franks
Coding is now one of the most sought-after skills. Coding is used in every industry. Even in fields that do not directly require coding, programming indicates transferable skills. Programming is becoming increasingly relevant to library science. Many libraries are incorporating digital resources, creating online catalogs, and developing digital archives, which require programming skills to maintain and update. Additionally, librarians often use programming languages to automate routine tasks, analyze data, and create user-friendly interfaces for library patrons. As technology continues to play a significant role in information management, programming skills are valuable for librarians to effectively navigate and utilize digital resources. With libraries increasingly depending on constantly evolving technology, library technologists often must know more, and more varied, technologies than ever.
You don’t need a computer science degree to become a programmer and learn coding languages. During this session we will discuss about why to learn coding, its role in day-to-day tasks, and different things one should know before learning coding.
For early-career librarians and library students interested in learning coding and going into a technology-focused area of librarianship it can be difficult to decide which programming language to learn in order to be the most marketable, or which language will be the most useful for a specific career path. We will discuss about popular languages also.
In summary, programming is important in library and information science for the automation of tasks, data management, web development, information retrieval, and keeping up with emerging technologies. By learning to program, LIS professionals can enhance their skills and contribute to the development of innovative and efficient solutions in the field.
Learning Objectives:
Participant will be able to see the benefits and importance of learning coding in their work and monetize on the available data to get insights and take intelligent decision to with the help of data. At the end of session participants will be able to start thinking about learning coding and apply in their work.
Instructor:
Pramod Gupta
The session builds on the Identifying opportunities and challenges session to dig deeper into library strategies for responding to AI. It will use tools such as policy analysis, force field analysis, capability modelling and roadmapping to engage participants in a process to define considerations for a strategy relevant to their context.
Learning Objectives:
- To appreciate the importance of library positioning in relation to relevant national, sectoral and institutional policy trends
- To be able to apply force field analysis to the issue of AI adoption
- To be able to apply AI capability modelling to their own context
- To understand the steps in designing a roadmap for AI in their context
Instructor:
Andrew Cox
Day 3 (May 30)
Times below are in US Eastern TIme. Click on time to find the time in your local time zone.
AI in Information Search and Discovery
While AI has long had a role in search tools such as recommender systems, AI’s search and retrieval roles have become more directly user-facing with the advent of generative AI, with people turning to generative AI for everything from answers to simple questions to introductions to entire topics. In this session, participants will engage with AI for information search and discovery, exploring the potential benefits and challenges, and practicing inputting prompts to examine the quality of the results returned by GenAI.
Learning Objectives:
- Become familiar with the roles that AI plays in information search and discovery.
- Examine the risks and benefits of AI-driven information search.
- Practice creating information search prompts for generative AI and develop familiarity with the types of information differing prompts return.
Instructor:
Alamir Novin
Despite their many differences, AI and blockchain have similar lessons for librarians in the adoption of new technologies. Both AI and blockchain have been extremely hyped, with enthusiasts heralding them as revolutions. However, for librarians, archivists, and other information professionals tasked with evaluating the suitability of AI, blockchain, and other emerging technology solutions for their work, it is necessary to develop the ability to critically evaluate the affordances, constraints, and impacts of such solutions, and to understand their role in the broader information ecosystem. In this session, participants will look at these two technologies to identify affordances and constraints based on both the design of solutions and the requirements, examining their intersection through the problem of data privacy.
Learning Objectives:
- Evaluate the affordances and constraints of emerging technologies, including AI and blockchain.
- Examine the impact of solution design on the appropriateness of the solution to particular information problems.
- Apply technology evaluation to a particular information problem.
Instructor:
Darra Hoffman
- Gain a foundational understanding of text analysis, a crucial research methodology for text miners.
- Learn SÉANCE and understand the designed features tailored for text analysis.
- Develop practical proficiency in utilizing SÉANCE to compute linguistic features and employing Python for statistical analysis.
- Attain proficiency in interpreting the results.
Instructor:
Lingzi Hong
Xiaoying Song
Evaluating a model's output is as crucial as its training phase. For library practitioners, understanding how to effectively assess algorithms of various types—including classification, clustering, retrieval, and generation—is essential for advancing their library services. This session will delve into the methodologies and metrics essential for evaluating machine learning models, focusing on information retrieval systems. Participants will explore key performance indicators, such as precision, recall, and F1 score, alongside advanced topics, such as ROC curves and confusion matrices. The session will emphasize retrieval evaluation metrics, which are crucial for enhancing search and recommendation systems within libraries. Additionally, we will discuss the importance of interpretable models that library staff can trust and understand, enhancing their ability to make data-driven decisions. The session will also address benchmarking, outlining what constitutes a robust benchmarking system and how to set benchmarks relevant to specific needs.
Learning Objectives:
- Evaluating outputs produced by algorithms of different natures – classification, clustering, retrieval, and generation.
- Exploring retrieval evaluation metrics.
- Using evaluation as a measure of interpretable model making.
- Understanding the characteristics of a good benchmarking system and learning to establish effective benchmarks.
Instructor:
Satanu Ghosh
Day 4 (May 31)
All times are US Eastern. Click on time to find the time in your local time zone.
In this talk, we will delve into the world of chatbots and their transformative potential in library services. This talk will explore the intersection of technical solutions and user experience design, providing librarians with the tools and knowledge needed to implement effective chatbot systems that enhance user engagement and information access. We will focus on understanding chatbot technologies, designing for user experience, and practical application in libraries.
Learning Objectives:
- Learn the fundamentals of chatbot technologies, including backend and frontend technical solutions.
- Understand the principles of user experience design tailored to chatbot applications.
- Explore real-world examples and case studies where chatbots have been successfully implemented in libraries.
Instructor:
Jiangen He
Large Language Models (LLMs) are rapidly increasing in popularity and becoming a disruptive technology in various sectors, including library and information science. They can be very useful tools that can enhance our experience and make our lives easier. However, these systems are black boxes with little to no understanding of what is going under the hood. This session will allow the participants to explore the inner workings of large language models, how they are trained on vast datasets, and their ability to produce coherent, contextually relevant content. We will explain the layers of LLM training and how we can enhance the human-LLM interaction experience.
Learning Objectives:
- Understand the various layers of LLM training.
- Examine how LLMs work with external data sources.
- Demonstrate information retrieval and extraction capabilities with LLMs.
- Apply conversational information-seeking techniques in library settings.
Instructor:
Sanatu Ghosh
Souvick Ghosh
The potential for AI to harm marginalized communities, including people of color, religious minorities, and LGBTQ+ people, is well documented: Black people have been misidentified as police suspects by facial recognition technologies, AI-powered search and retrieval affiliates hijabi women with “violence” and “terrorism,” and the algorithms underlying airport scanners cannot identify gender non-conforming people. Given these challenges, how can libraries leverage the potential AI in just and inclusive ways? How can librarians identify the equity risks in AI tools and mitigate appropriately? In this session, participants will explore the central issues surrounding equity, diversity, inclusion and social justice in AI projects, as well as tools and frameworks for addressing these issues, including cultural competence and humility.
Learning Objectives:
- Understand the ways in which AI technologies have disparate impacts on different communities.
- Examine the ways in which AI projects can pose equity, diversity, inclusion, and social justice concerns in libraries.
- Acquire familiarity with principles and approaches to examine and address EDI and social justice concerns in AI projects.
Instructor:
Vishnu Pendyala
The presentation identifies central issues in the ethics of artificial intelligence, explains their relation to different conceptions of AI (narrow, general), and their evolution from information ethics. It then reviews AI ethics principles and key ethical concepts and discusses the importance of using these frameworks and concepts in policy development and decision making
Learning Objectives:
After attending this presentation, participants will be able to:
- Identify and explain central AI issues \ risks as they impact individuals and society.
- Identify ways in which AI issues \ risks arise in LIS contexts.
- Use AI ethical principles and concepts to analyze issues when formulating policy or supporting decisions.
Instructor:
Norman Mooradian