Libraries and AI Response: Strategic Approaches for Library Services

The YouTube video titled "Libraries and AI Response: Strategic Approaches for Library Services" delves into the role of artificial intelligence (AI) within libraries and offers strategic insights on how libraries can develop AI strategies. Presented by Don Means, Andrew Cox, and Eric Bokenstein, the video highlights the challenges, opportunities, and potential future role libraries could play in the era of AI. It equips you with the knowledge to navigate this evolving landscape.

Introduction

The discussion begins with a general overview by Don Means, emphasizing the Swiss-army-knife role of libraries as multifaceted institutions. Libraries do far more than manage books—they offer various public services that often go unnoticed. The video focuses on how libraries can respond strategically to AI in their services, with examples of current AI implementations and future trends.

The Role of Librarians in AI

One of the recurring themes is the evolving role of librarians in the age of AI:

• Curators of Information: Librarians could become "prompt engineers," guiding users in interacting with AI systems to maximize their benefits.
• Trust and Human Interaction: Despite AI's growing role, human judgment, empathy, and the ability to ask nuanced questions will remain essential.
• Promoting Digital Literacy: Librarians must continue promoting digital literacy, ensuring users can critically engage with AI tools.

Key Takeaways

• Libraries as Leaders in AI Ethics: Libraries uniquely promote ethical AI practices and ensure that marginalized voices are not left out of AI developments.
• Strategic Collaboration: Collaboration among libraries, tech companies, and users is critical to developing effective AI systems.
• Empowerment through AI: AI should be seen as a tool to enhance library services, but its implementation must be carefully managed to avoid reinforcing biases or creating new inequalities.

AI and Libraries: Current and Future Context

Andrew Cox leads the first presentation and discusses libraries' strategic responses to AI. He outlines several critical aspects of AI that libraries need to consider:

• Traditional Uses of AI: AI is already used in spam filtering, transcription, translation, and recommendation systems. While helpful, these applications come with issues of bias, especially concerning non-English-speaking users and minorities.
• Descriptive AI: This allows libraries to improve search capabilities through voice and image recognition technologies, which can help users navigate vast collections more effectively. However, if designed carefully, these systems could be better and reinforce existing biases.
• Generative AI: Tools like ChatGPT, which can generate content, code, and answer questions, present exciting possibilities and challenges. Libraries must know its limitations—such as misinformation, hallucinated content, biased results, and fabricated citations.

Strategic Frameworks for AI in Libraries

Andrew Cox discusses how libraries might develop strategic responses to AI:

• Proof of Concept Projects: Many libraries are already experimenting with small AI projects to explore its potential.
• Collaboration: Libraries should collaborate with technology providers to improve AI's role in library functions.
• Engaging Users: Libraries should engage with users to understand how they are utilizing AI and support their needs accordingly.
• Staff Training: Encouraging library staff to experiment with AI tools helps develop their skills and promotes innovative use cases.
• Strategy Development: Cox advocates for libraries to define strategic goals related to AI, such as improving services or creating a vision for AI aligning with library values.

Libraries as AI Intermediaries

Eric Borenstein presents the Netherlands AI Coalition, a national initiative that brings together cultural institutions like museums, libraries, and archives to collaborate on AI projects. He highlights the importance of community-driven projects like the "AI Parade," a traveling AI exhibition that engages the public and raises awareness about AI's role in society. The AI Parade has been highly successful, reaching over 1.5 million people quickly, showing that AI awareness is a critical educational focus for libraries.

Ethical Concerns and Responsible AI

The discussion turns to the ethical use of AI. Andrew and Eric stress that AI systems must be transparent, safe, and human-centered. They acknowledge the challenges related to bias, misinformation, and privacy concerns. Ethical frameworks must guide AI's implementation, ensuring it benefits library users while maintaining inclusivity and accessibility.

Decolonizing Libraries and Archives: Fostering Indigenous Knowledge and the Role of AI

Decolonizing Libraries and Archives: Fostering Indigenous Knowledge and the Role of AI

The discussion between Alexia Hudson-Ward and Jordan T. Clark is a thought-provoking continuation of their earlier dialogue on decolonization efforts in libraries. It focuses on collection practices, archival work, and the increasing role of artificial intelligence (AI) in academic settings. This conversation addresses how libraries can actively decolonize their spaces, collections, and practices while navigating the challenges of emerging AI technologies.

Decolonizing Libraries: Where to Begin

Clark opens the discussion by tackling one of the most frequent questions he receives: "What books should I buy to decolonize my library?" He emphasizes that decolonizing libraries is not merely about acquiring a checklist of books written by Indigenous authors but about a holistic, transformative mindset shift that inspires and motivates every aspect of library practice.

Key Takeaways:

1. Beyond Book Collections: While having literature from Native voices is essential, libraries must also physically and conceptually transform their spaces. Clark recounts an example where a librarian regularly rearranged the library to challenge students to engage with non-Western perspectives through rotating art pieces, quotes, and visual stimuli. This active engagement creates a dynamic learning environment.
2. Engagement Over Passivity: Libraries should not be passive repositories but active spaces inviting users to engage critically with their collections. For example, the librarian can reach the entire student body in high school libraries, making the library an interactive classroom.
3. Inclusive Spaces: Clark highlights Julie Fiveash's work at Harvard's Tozzer Library, which goes beyond books to include zines and other non-traditional forms of knowledge. This promotes the idea that libraries can be reimagined to offer more inclusive, non-Western perspectives.

Archives and Special Collections: Addressing Colonial Legacies

The conversation then shifts to archives and special collections, traditionally dominated by Western narratives. Clark discusses the importance of rethinking how libraries approach these collections, especially when dealing with materials historically used to oppress Indigenous communities.

Challenges and Opportunities in Archival Work:

1. The John Eliot Bible: Clark brings up the example of Harvard's Houghton Library, which houses the John Eliot Bible—the first Bible printed in the Western Hemisphere, but in the Wampanoag language, used to convert Indigenous people to Christianity forcibly. Rather than solely allowing this object to reflect a colonial narrative, it should be used to tell a fuller, more nuanced story, including its role in the modern-day Wampanoag language revitalization project.
2. Holistic Storytelling: Archivists should focus on telling the entire story of these objects, not just their colonial history. Centering Indigenous voices, such as the work of Jesse Little Doe Baird in language revitalization, helps shift the narrative away from colonial oppression toward empowerment and cultural survival.
3. Partnerships and Networks: Libraries must build coalitions with Native communities and other institutions to foster an inclusive network that elevates marginalized voices. Sharing knowledge across institutions allows for a broader understanding of how to approach decolonizing archives and special collections.

Decolonizing AI: Challenges and Cautions

Hudson-Ward and Clark then explore how artificial intelligence is both a tool for advancing Indigenous knowledge and a source of concern, as it can perpetuate existing biases rooted in colonialism.

AI in Academia and Indigenous Knowledge:

1. AI's Dependence on Existing Data: Large language models (LLMs) like ChatGPT do not create knowledge; they scrape information from existing sources. Clark notes that AI will only perpetuate those biases if the scraped data is colonized or biased. This is particularly concerning for Native communities whose knowledge has historically been stolen or misrepresented.
2. Amplifying Erasure: AI systems trained on colonized data sources could unintentionally amplify the erasure of Native voices rather than uplift them. The danger lies in reinforcing problematic narratives if Indigenous perspectives are separate from the foundational data these systems learn from.
3. Representation in AI Development: Indigenous voices must be included in developing and deploying AI technologies to combat this. Clark stresses that without Native representation in AI development, these technologies are unlikely to advance Indigenous knowledge effectively.

Building Trust and Relationships with Indigenous Communities

One of the central themes in the discussion is the necessity of building trust between institutions and Native communities. This trust is vital for fostering knowledge sharing and ensuring that Indigenous knowledge is not appropriated or misused.

Recommendations for Libraries and Academic Institutions:

1. Fostering Long-Term Relationships: Clark underscores that libraries must build consistent, trustworthy relationships with Native communities. This will allow Indigenous voices to be centered on collecting, preserving, and disseminating knowledge.
2. Representation Matters: Institutions must ensure that Indigenous people are actively involved in decision-making processes, whether in hiring Native staff, curating collections, or creating AI tools. Representation within institutions is essential for meaningful progress.
3. Mutual Benefit: For Native communities to share their knowledge, institutions must provide reciprocal benefits, such as increased educational opportunities for Native students or collaborative partnerships that benefit the community.

AI, Cultural Heritage, and the Future

The conversation concludes with reflections on how AI technologies can be both a challenge and an opportunity for Indigenous communities. Hudson-Ward mentions how African nations call for AI tools that better represent their cultural artifacts and languages, which mirrors the need for Native American representation in AI systems.

Balancing AI's Benefits and Risks:

1. Developing Culturally Relevant AI Tools: Clark emphasizes the need for AI tools to process Indigenous languages and cultural artifacts accurately. Without such developments, AI risks perpetuating stereotypes or misrepresenting Indigenous knowledge.
2. Collaborative AI Development: Universities and tech companies must collaborate with Indigenous communities to build AI systems that reflect their cultural and historical realities. This will help avoid the pitfalls of technology that misinterprets or distorts Indigenous knowledge.

Moving Forward with Intentionality

In this engaging conversation, Clark and Hudson-Ward offer valuable insights into how libraries and archives can better support Indigenous communities by decolonizing their practices and spaces. Whether through rethinking how collections are curated, building trust with Native communities, or ensuring Indigenous representation in AI development, libraries can take multiple paths to foster a more inclusive environment.

The conversation highlights that decolonization is not a checklist but a mindset and practice that must permeate every layer of an institution—from book collections to AI systems. By focusing on building relationships, fostering inclusivity, and using technology responsibly, libraries and academic institutions can begin to reverse colonialism's legacy and create spaces where Indigenous knowledge is truly valued.

AI Knowledge Graphs and Scholarly Research: Leveraging Technology for Improved Academic Research

AI Knowledge Graphs and Scholarly Research: Leveraging Technology for Improved Academic Research

In a time when artificial intelligence (AI) is rapidly transforming numerous sectors, libraries and academia are not immune to its effects. The video hosted by Leah Hines features Ruth Pickering, co-founder of Yewno, and Matthew Ismail, Director of Collection Development at Central Michigan University. The discussion provides valuable insights into the transformative potential of AI and knowledge graphs in scholarly research. It explores how libraries can integrate these technologies to enhance information discovery and research efficiency, inspiring a hopeful vision for the future of academic research.

Introduction: The Role of AI in Scholarly Research

The conversation opens with the recognition that AI is becoming a vital technology, offering solutions for the ever-growing volume of information. Libraries and research institutions face a crucial challenge: managing and organizing massive data while ensuring that relevant information is easily accessible. AI-powered systems, such as knowledge graphs, offer a way forward by providing a more dynamic and contextual approach to search and discovery.

Key Takeaways:

1. Knowledge Graphs and AI: Knowledge graphs allow for dynamic, real-time organization of data that reveals relationships between concepts rather than just indexing content through keywords.
2. Scalability of AI: AI enables libraries to manage large volumes of information and maintain up-to-date resources by continuously ingesting new data and updating existing relationships between pieces of information.
3. Dynamic Nature of Content: AI-driven platforms can understand evolving relationships between data, which is beneficial for scholarly research that often deals with interdisciplinary content.

Knowledge Graphs: A Powerful Tool for Organizing Information

Ruth Pickering provides an overview of how knowledge graphs operate. Traditional systems typically rely on static keyword-based methods for organizing information, which may not adapt to changes over time. In contrast, knowledge graphs represent an evolving, dynamic system that continuously updates relationships between data.

Benefits of Knowledge Graphs:

• Contextual Understanding: Knowledge graphs connect concepts and topics based on their relationship, offering users a contextual understanding of the data they are exploring. For instance, if a user searches for the term "depression," the system can differentiate between economic depression, mood disorders, or geological phenomena based on the surrounding context.
• Disambiguation: The ability to distinguish between various meanings of a word or concept is a crucial feature of knowledge graphs, significantly improving the precision of search results.

Real-World Applications: AI in Library Search Systems

The video emphasizes the practical applications of AI in library settings. One such use case is improving search engines within academic libraries by integrating knowledge graphs. These graphs allow researchers to discover more relevant and accurate results by exploring the relationships between different concepts.

Example Scenarios:

1. Search Optimization: AI-based systems can suggest multiple possible meanings or related topics when users enter broad or ambiguous terms. For example, searching for "undocumented" could lead to various results, such as immigration policies or healthcare for undocumented individuals. AI can help refine the results based on what the user intends to find.
2. Interdisciplinary Research: Knowledge graphs allow for more seamless research across academic disciplines. In the future, great discoveries are expected to come from interdisciplinary collaboration, and AI will be essential for enabling researchers to collaborate more effectively across fields.

AI's Role in Addressing Information Overload

The speakers discuss how the sheer volume of scholarly publications presents a significant barrier to efficient research. However, AI offers a way to address this challenge by automating specific processes, such as identifying key trends in research or helping users filter through large datasets. This emphasis on AI's role in addressing information overload can make the audience feel relieved and reassured about the future of research.

Efficiency Gains:

• Automated Content Discovery: AI can scan, ingest, and organize newly published content in real time, ensuring that research libraries remain up-to-date with the latest studies and findings.
• Personalized Research: AI tools can tailor recommendations for individual researchers based on their past activities and interests, making it easier to find new and relevant materials.

Visualization and Data Discovery

One of the standout features of knowledge graphs is their ability to represent complex relationships between concepts visually. This visualization allows researchers to quickly see connections between various topics or scholars, facilitating a more intuitive understanding of the data.

Visualization Advantages:

• Conceptual Mapping: Rather than being presented with a long list of articles or books, users can explore a network of interconnected ideas. This method encourages more profound research and helps uncover connections that might take time to be evident in traditional searches.
• Enhanced Search Experience: Visualizing the connections between related terms and concepts helps researchers quickly narrow their focus and explore relevant but previously overlooked areas of study.

AI and Special Collections: Institutional Repositories

The conversation touches on how AI can also assist libraries with managing institutional repositories, which often hold valuable but difficult-to-discover content, such as dissertations, research papers, and special collections. AI technology can make these collections more discoverable by organizing them dynamically and linking them to related research outside the institution.

The Future of Libraries with AI

The discussion concludes by envisioning the future of libraries as AI continues to evolve. Libraries are poised to play a critical role in ensuring ethical AI usage and fostering AI literacy among staff and patrons. AI can be a game-changer for libraries and research institutions by improving how information is organized and enhancing user experience, making research more efficient and impactful.

Libraries that embrace AI tools such as knowledge graphs will be better equipped to manage the demands of modern research, paving the way for future discoveries.