Top Impacts of Library and Information Science in the AI Era

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Library and Information Science (LIS) plays a vital and continuously evolving role in the rapidly advancing AI era by serving as the fundamental authority for ethical, curatorial, and innovative approaches to managing vast amounts of AI data, ensuring the protection of individual privacy, and transforming user services to meet new technological demands.

LIS professionals contribute invaluable expertise in data curation, metadata management, and classification systems that not only protect AI applications from bias and data-quality problems but also actively promote AI literacy and advocate for strong privacy protections.

Top Impacts of Library and Information Science in the AI Era

As AI increasingly automates routine and repetitive tasks, such as cataloging and indexing, LIS experts take the lead in establishing ethical governance frameworks, providing advanced research support, and fostering meaningful community engagement. This, in turn, shapes both the development of AI systems and the informed, responsible use of these technologies.

Table of Contents

Introduction to Library and Information Science in AI

Library and Information Science (LIS) has traditionally concentrated on the critical tasks of organizing, preserving, and providing equitable access to a wide range of knowledge resources. This foundation is built upon established principles such as metadata standards, classification systems, and the development of user-centered services designed to meet diverse informational needs.

However, in the rapidly evolving era of artificial intelligence, these core functions have undergone a significant expansion. Emerging technologies like:

Machine Learning
Natural Language Processing
Generative AI

They are profoundly transforming how information is created, curated, accessed, and analyzed. These advancements impact various domains, including:

Library Services
Academic Research
Data Governance
Community Knowledge Interfaces

As a result, Library and Information Science professionals are now required to adapt by gaining expertise in AI integration, upholding ethical standards for data use, and spearheading innovative approaches. This evolution is essential for professionals to maintain their relevance and effectiveness in an increasingly algorithm-driven and technologically complex world.

This transformation carries deeply profound and far-reaching significance for three key audiences, each uniquely positioned and prepared to leverage their expertise in Library and Information Science (LIS) in the rapidly evolving landscape shaped by the rise of artificial intelligence (AI):

Library and Information Science Professionals and Students (The Practitioners): Gain valuable validation for your enduring and essential skills, such as curation, while simultaneously acquiring a range of high-value, cutting-edge competencies, including AI ethics auditing, prompt engineering, and data quality control. These skills will empower you to effectively lead your career evolution and play a pivotal role in the responsible and ethical deployment of artificial intelligence technologies in various professional environments.

Information and Technology Developers (The Builders): These professionals acknowledge and embrace LIS principles—such as provenance tracking and bias mitigation—as fundamental components in the creation of transparent, fair, and trustworthy AI systems. They actively advocate for the integration of these principles early in the design and development phases, emphasizing that doing so significantly enhances data reliability and integrity while effectively minimizing ethical risks and potential biases throughout the AI lifecycle. This approach ensures that the AI solutions they build are not only technically robust but also ethically sound and socially responsible.

Institutional & Policy Leaders (The Decision-Makers): Gain access to comprehensive, evidence-based return on investment (ROI) arguments designed to support funding for LIS-led initiatives focused on AI literacy training and governance. These resources help address and mitigate significant societal risks such as the amplification of bias and breaches of privacy, while simultaneously promoting the inclusive adoption of emerging technologies across diverse communities. This approach ensures that decision-makers have the necessary information to justify investments that balance innovation with ethical responsibility and social equity.

By strategically positioning Library and Information Science as the essential ethical and curatorial backbone of the entire AI ecosystem, this detailed exploration underscores the significant and measurable impacts on multiple critical areas, including data quality, user privacy, and innovative service development.

It empowers a diverse and broad range of stakeholders by equipping them with the essential insights, knowledge, and practical tools needed to effectively navigate the increasingly complex opportunities and challenges that are presented by artificial intelligence. This enables them to act in a responsible, thoughtful, and well-informed manner throughout their interactions with AI technologies.

Key Concepts and Theories in Library and Information Scienceand AI

Understanding the significant impact of Library and Information Science (LIS) on the rapidly evolving field of Artificial Intelligence (AI) fundamentally depends on grasping core LIS concepts and their effective, seamless adaptations within diverse AI ecosystems.

These foundational ideas, which are deeply rooted in decades of rigorous information management practice and scholarly research, offer a crucial theoretical framework that empowers LIS professionals to effectively regulate data, advocate steadfastly for users’ needs, and actively drive innovation and ethical advancements amid the fast-paced rise and integration of AI technologies.

Metadata and Classification

LIS’s expertise in creating standardized metadata, such as Dublin Core or MARC standards, plays a crucial role in ensuring that AI training data is thoroughly and accurately described, traceable, and enriched with context. This meticulous attention to metadata directly enhances AI reliability and fairness by enabling effective provenance tracking and facilitating the detection of biases within datasets.

In practical AI applications, this expertise translates into the development of sophisticated automated cataloging tools that generate detailed metadata tags through natural language processing (NLP) techniques, significantly improving the searchability and discoverability of information.

At the same time, human oversight continues to be absolutely essential in order to maintain a high level of contextual accuracy. This careful supervision helps to ensure that the metadata truly reflects the genuine meaning and subtle nuances of the content being described. Without the involvement of human judgment, these important aspects could be easily overlooked or misinterpreted.

Data Curation

Mirroring the established practices of traditional collection development, AI-era data curation involves the careful selection, organization, and ongoing preservation of datasets. This process is essential to maintain high standards of quality, diversity, and provenance, which collectively help prevent biased or unfair outcomes in machine learning models.

Library and Information Science (LIS) professionals bring their expertise to this field by thoroughly auditing training data sets, much like curating diverse and balanced library collections. Their work ensures that these datasets accurately reflect equitable representation across different populations and are maintained for long-term usability and integrity within digital preservation efforts.

This comprehensive and holistic approach plays a crucial and indispensable role in safeguarding the ethical use of data while also ensuring the long-term sustainability and responsible management of data practices across a wide range of AI applications and systems. By integrating these principles, it helps maintain trust, accountability, and transparency throughout the entire data lifecycle.

Information Retrieval

AI-powered search and recommendation systems extensively draw on foundational Library and Information Science (LIS) principles, including controlled vocabularies and relevance ranking, to significantly refine their algorithms.

These systems deliver highly user-centered results by employing advanced techniques such as semantic search and personalized content suggestions that are tailored based on an individual user’s behavior and preferences.

Additionally, natural language processing (NLP) and machine learning technologies greatly enhance these systems by effectively processing natural language queries, enabling a more intuitive and seamless discovery experience for users. At the same time, LIS frameworks ensure that the outputs generated by these AI systems remain unbiased, ethically sound, and contextually relevant to the user’s specific information needs.

Ethical Stewardship

Firmly grounded in the core tenets of Library and Information Science (LIS), including intellectual freedom, user privacy, and social responsibility—principles that are clearly codified in established frameworks such as the American Library Association (ALA) Code of Ethics—ethical stewardship plays a crucial role in shaping responsible AI policies.

These policies are designed to actively combat discrimination, rigorously safeguard data privacy, and consistently promote transparency in all AI applications. By doing so, LIS professionals position themselves as an essential counterbalance to the potential risks posed by AI technologies, contributing valuable insights that inform comprehensive guidelines focused on algorithmic accountability and ensuring equitable access to information and services within library environments.

In Summary

These interconnected and deeply intertwined concepts firmly establish LIS professionals’ roles as absolutely indispensable AI data regulators, passionate user advocates, and visionary innovation leaders. They serve as crucial bridges that connect human-centered information practices with the rapidly evolving and cutting-edge technology landscape, ensuring ethical, effective, and user-focused advancements in the field.

Current Trends and Developments in LIS and AI

Recent literature and emerging practices from the years 2024 to 2025 reveal that artificial intelligence is profoundly reshaping the field of Library and Information Science (LIS). This transformation is characterized by the automation of routine and repetitive tasks, allowing professionals to focus more deeply on complex and nuanced areas.

At the same time, AI is significantly elevating the importance of human expertise, especially in critical domains such as ethics, careful curation of information, and fostering innovation within the field. This evolving landscape highlights a dynamic interplay where technology enhances human roles rather than replaces them, leading to new opportunities and challenges for LIS practitioners.

Skill Evolution

Traditional LIS skills such as descriptive cataloging and handling basic reference queries are increasingly being automated by advanced AI tools, which is driving a significant shift in the roles of information professionals toward more high-value and strategic positions as knowledge navigators. Emerging critical competencies now include:

AI ethics auditing to identify and address biases within algorithms and datasets
Prompt engineering aimed at optimizing the performance of large language models (LLMs)
Data analytics focused on assessing and ensuring the quality and integrity of datasets

Additionally, proficiency in basic coding languages like Python has become essential for automating routine tasks, while skills in UX design are necessary to create intuitive and user-friendly interfaces. Advanced digital curation techniques are also vital for maintaining sustainable and well-organized data ecosystems.

In this evolving landscape, Library and Information Science practitioners increasingly take on the responsibility of facilitating AI literacy workshops and providing training on the ethical use of AI technologies, thereby positioning themselves as influential leaders within interdisciplinary teams and organizations.

AI-Driven Tools in Library and Information Science

Artificial intelligence significantly enhances library and information science (LIS) operations by providing advanced personalized recommendation engines that carefully analyze detailed user behavior and patterns. Intelligent chatbots offer continuous 24/7 reference and support services, ensuring users can access assistance at any time.

Furthermore, natural language processing (NLP) technologies facilitate seamless multilingual search capabilities and effective content summarization, making information more accessible across language barriers. These innovative AI tools greatly improve the efficiency of information retrieval processes and enrich the overall user experience, as demonstrated by sophisticated systems such as generative AI for refining complex search queries.

However, despite these advancements, the professional judgment of human LIS experts remains essential to thoroughly validate AI-generated outputs, ensuring factual accuracy, maintaining contextual relevance, and actively mitigating inherent biases. This human oversight is crucial to prevent potentially serious errors, especially in high-stakes or sensitive environments where precision and reliability are paramount.

Privacy and Ethical Challenges

Algorithmic biases present in AI training data, combined with significant privacy risks stemming from surveillance-like tracking practices, require strong and proactive oversight by Library and Information Science (LIS) professionals. This oversight must be firmly grounded in libraries’ deep-rooted tradition of upholding intellectual freedom and protecting user confidentiality at all costs.

Library and Information Science professionals play a leading role in the ethical deployment of AI technologies by thoroughly auditing data governance pipelines and rigorously enforcing key principles such as transparency, fairness, and non-discrimination.

Their efforts are guided by authoritative frameworks like the American Library Association (ALA) guidelines, which emphasize responsible information management. This distinctive position allows LIS experts to effectively advocate for the protection of user data, especially amid growing concerns over the opaque and often unexplained processes behind generative AI models, ensuring that users’ privacy rights are safeguarded in an increasingly complex digital environment.

Institutional Recognition

Academic and public libraries are increasingly embedding comprehensive AI literacy programs and ethical policymaking frameworks into their operations, with Library and Information Science (LIS) experts playing a crucial role by co-leading governance initiatives related to artificial intelligence. For example, prestigious universities such as Northwestern regularly host symposia and conferences focused on exploring AI’s evolving role and future impact within library settings.

At the same time, many institutions are incorporating LIS professionals directly into AI research consultations, ensuring that expertise in information management and ethical considerations informs technology development. Real-world examples of this trend include librarians at various U.S. academic libraries who have taken the lead in developing detailed AI ethics workshops specifically designed for faculty members.

These librarians also actively collaborate with researchers to guide on selecting appropriate AI tools, thereby extending the influence of LIS professionals beyond traditional library services into the broader realms of technology ethics and responsible data stewardship.

In Summary

These emerging trends highlight the significant shift of Library and Information Science (LIS) from merely providing operational support to taking on a more strategic and authoritative role within organizations. This transition is crucial in making certain that artificial intelligence technologies are utilized in ways that enhance and amplify equitable access to information, rather than diminishing or undermining it in any capacity.

By fully embracing this important strategic position, Library and Information Science professionals have the unique opportunity to actively influence and shape the way AI tools are designed, developed, and implemented.

Their involvement can help ensure that these technologies promote fairness, equity, and inclusivity in the availability and accessibility of information for all users, regardless of background or circumstance. This proactive role allows them to advocate for ethical considerations and user-centered approaches in the evolving landscape of information technology.

The Curatorial Impact: Library and Information Science as the Data Regulator

Library and Information Science (LIS) professionals play a crucial and leading role in addressing the challenges posed by flawed and biased artificial intelligence systems. By utilizing their deep expertise in metadata standards, precise cataloging techniques, and thoughtful collection development strategies, they provide essential and robust data curation practices.

These skills are directly applicable and highly valuable in AI contexts, where LIS specialists ensure that training datasets are meticulously well-described, contextually grounded, and representative of diverse perspectives.

Additionally, they work diligently to identify and eliminate biases, helping prevent skewed or unfair outcomes. This careful attention is especially important in sensitive applications such as hiring algorithms, medical diagnostics, and other critical decision-making systems, where biased data could otherwise perpetuate inequities and harm marginalized groups.

Applied Practices in AI Data Regulation

Library and Information Science methods adapt seamlessly and effectively to AI pipelines, with skilled professionals applying a variety of well-established and proven techniques:

Auditing and Cleaning Datasets: LIS experts meticulously examine AI training data to verify its provenance, meaning the tracking of its original source, and to ensure it is truly representative of the intended population or subject matter. They carefully identify and remove duplicates, fill in gaps, and eliminate sources that might introduce bias or skew the data—similar to the careful process of weeding and curating library collections. This thorough cleaning process is essential to promote fair, balanced, and accurate model performance across a wide range of applications.

Designing Classification Schemes: By drawing from well-established standards such as Dublin Core, Library and Information Science (LIS) professionals develop detailed taxonomies and ontologies that significantly improve AI transparency and explainability. These thoughtfully crafted classification schemes allow users to clearly trace the paths of AI decision-making processes and facilitate thorough auditing to detect any hidden biases or inconsistencies within the system. This approach ensures greater accountability and trust in AI technologies by making their inner workings more understandable and accessible to users.

Quality Control in Data Pipelines: Similar to the meticulous process of traditional cataloging, Library and Information Science (LIS) incorporates continuous validation protocols throughout data pipelines. These protocols utilize advanced AI-assisted tools designed to automate the generation and organization of metadata efficiently. Despite this automation, human experts remain actively involved to oversee the process, ensuring the accuracy of the information and maintaining cultural sensitivity. This combined approach balances technological innovation with essential human judgment to uphold high standards in data quality.

Real-world examples significantly amplify this impact and demonstrate its practical applications: At Indiana University, a series of colloquia delve deeply into the use of generative AI technologies to enhance and augment data discovery processes within complex curation workflows.

Meanwhile, libraries around the world are collaborating extensively on the development of domain-specific knowledge graphs, aiming to build ethical, high-quality datasets that serve specialized research fields more effectively. Dr. Sarah Johnson, a respected expert from Oxford’s Bodleian Library, highlights this transformative shift by stating,

“Librarians are evolving from their traditional role of merely curating existing knowledge to actively co-creating customized and tailored products alongside AI systems, all while maintaining a strong commitment to quality and ethical standards.”

Through these comprehensive and carefully designed processes, Library and Information Science establishes fair, transparent, and reliable foundations for the development and deployment of AI systems. This makes the profession absolutely indispensable to the ethical advancement of artificial intelligence technologies.

By ensuring these standards, Library and Information Science plays a crucial role in mitigating a wide range of risks, including the amplification of harmful societal biases and other unintended negative consequences that could arise from poorly managed AI implementations.

The Ethical and Privacy Impact: LIS as the User Advocate

Libraries and information centers have historically been strong advocates for privacy, intellectual freedom, and equitable access to information. These foundational principles are deeply embedded in professional codes such as the American Library Association (ALA) Bill of Rights.

Today, these values are increasingly critical as they extend powerfully into the realm of artificial intelligence, where concerns about pervasive data surveillance and the use of opaque algorithms pose significant threats to user rights and freedoms.

Library and information science (LIS) professionals play a crucial and leading role in addressing these complex challenges by actively spearheading initiatives to develop comprehensive, effective, and responsible AI ethics policies.

They are deeply involved not only in the creation but also in the implementation and continuous, rigorous auditing of these policies to ensure ethical standards are upheld throughout the use of artificial intelligence technologies.

Initiatives like the EL-AIL Framework (Ethical Leadership for AI in Libraries) exemplify this leadership, aiming to protect user data privacy, combat algorithmic bias and discrimination, and promote the transparent and accountable deployment of AI technologies within library settings and beyond.

Key Roles in Ethical Advocacy

Library and information science expertise positions professionals as essential frontline defenders by empowering them through a series of carefully targeted and strategic actions designed to maximize impact and effectiveness in their roles:

Championing User Privacy: Actively advocating for and implementing anonymized data practices in the design and development of AI services, such as establishing robust facial recognition access controls, while simultaneously enforcing stringent confidentiality measures within recommendation systems to effectively prevent any form of user tracking or data misuse.

Promoting Greater Transparency and Accountability: Conducting thorough audits of black-box AI models to identify and address bias, while enhancing explainability. By drawing on long-standing Library and Information Science (LIS) traditions, we advocate for the implementation of auditable decision trails within library analytics systems. This approach ensures that all algorithmic decisions can be traced, scrutinized, and understood, thereby fostering trust and fairness in the use of AI technologies in library contexts.

Interdisciplinary Participation: Actively serving on AI ethics committees and various policy-making bodies, a role exemplified in university symposia where librarians play a crucial part in shaping and influencing institutional guidelines and best practices regarding the use of generative AI technologies.

User and Institutional Education: Providing comprehensive workshops focused on digital rights, advanced plagiarism detection techniques, and ethical AI literacy. These sessions are designed to equip individuals and communities with the necessary knowledge and skills to effectively navigate the complex challenges of misinformation and address equity risks in the digital age. Through these educational initiatives, participants gain a deeper understanding of responsible digital practices, fostering informed and equitable engagement in technology-driven environments.

Case in point: KNUST librarians in Ghana hosted workshops on “AI in Libraries: Tools, Ethics, and Opportunities,” addressing data privacy and bias, while U.S. academic libraries integrate community-based learning to teach AI ethics, fostering multidisciplinary guidelines. As one study notes, “LIS ethics stand strongly in opposition to uncritical learning analytics,” reminding stakeholders of libraries’ social responsibility.

Library and information science’s steadfast and unwavering commitment to social equity makes the profession an essential guardian against the potential encroachment of artificial intelligence on fundamental civil liberties. This dedication ensures that technology is developed and implemented in ways that serve all of humanity inclusively and fairly, protecting the rights and dignity of every individual in society.

The Service Transformation Impact: LIS as the Innovation Leader

Artificial Intelligence (AI) automates many repetitive and time-consuming tasks within the field of Library and Information Science (LIS), such as descriptive cataloging and handling basic reference inquiries. By taking over these routine activities, AI frees up LIS professionals to focus on more complex, human-centered roles that combine technological skills with empathy, critical thinking, and specialized expertise.

This transformation allows Library and information science experts to become pioneers of innovation, helping to evolve libraries from traditional, static repositories of information into vibrant, dynamic hubs of knowledge and intelligence by the year 2035.

Transformed High-Value Activities

Freed from the constraints of routine duties and repetitive tasks, Library and information science professionals are now able to excel and demonstrate their full potential in areas such as:

Advanced Research Consultation: By integrating cutting-edge AI tools for prompt engineering and meticulous output validation, librarians take on the vital role of “Knowledge Synthesists.” In this capacity, they collaborate closely with artificial intelligence technologies to develop highly contextualized and comprehensive research packages. These packages are carefully curated from vast and diverse datasets, ensuring that the information provided is both relevant and insightful for complex research needs.

AI Literacy and Digital Empowerment Programs: Conducting comprehensive workshops focused on the ethical use of AI technologies, fostering critical evaluation skills for assessing generative AI outputs, and enhancing understanding of algorithmic processes. These initiatives align with frameworks like the British Library’s AI Transition Framework, which highlights the importance of community-first approaches to upskilling and ensuring that individuals are empowered to navigate the evolving digital landscape responsibly and effectively.

Community Engagement on Data Futures: Hosting a series of dynamic and interactive discussions focused on the far-reaching impacts of emerging technologies, such as immersive virtual reality experiences that bring historical knowledge systems to life, or thoughtful debates exploring the evolving role of artificial intelligence in society. These events aim to foster inclusive and diverse dialogues that encourage participation from all community members, ensuring a broad range of perspectives are heard and valued. By creating these spaces for open conversation, we hope to deepen understanding and collaboration around the future of data and technology in our lives.

Collaboration with Technologists: Actively co-developing innovative and accessible AI interfaces, including custom-designed knowledge graphs and interactive scholarly objects, aimed at significantly enhancing usability and user experience for a wide and diverse range of users across various fields and disciplines.

Real-world examples abound: Manchester University Library’s emerging tech head James Chen pioneers AI-driven experiential learning, while Oxford’s Bodleian shifts librarians to co-creators of tailored knowledge products. Dr. Sarah Johnson emphasizes, “We’re moving from curating existing knowledge to actively creating new knowledge products,” highlighting human-AI synergy.

This evolution demands ongoing development of technological fluency (e.g., Python basics, NLP), ethical awareness, and community skills, ensuring LIS remains influential in AI-powered environments. As Mohammed Al-Faraj notes, “Anyone can get facts from AI, but understanding context and trust—that’s human expertise”.

FAQs

Which Library and Information Science skills are becoming automated by AI?

Routine cataloging, classification, and basic reference services are increasingly automated by AI systems like natural language processing for metadata generation and chatbots for query handling, allowing Library and information science professionals to focus on complex tasks such as data curation, ethical oversight, and advanced research synthesis.

What new skills should LIS professionals acquire to thrive in the AI era?

Essential skills include understanding AI tools (e.g., chatbots, recommendation systems), data management and analytics, ethical AI considerations like bias mitigation, digital literacy instruction, basic programming (Python), machine learning knowledge, UX design, and continuous adaptability through training.

Why are Library and Information Science principles important for AI developers?

LIS principles in metadata standards, classification systems, and ethical stewardship ensure high-quality, transparent, and fair AI training data, directly mitigating biases, enhancing reliability, and supporting explainable algorithms in applications like search and personalization.

How can Library and Information Science professionals contribute to AI ethics?

Library and information science experts develop and audit policies for user privacy protection, algorithmic transparency, non-discrimination, and compliance with standards like ALA ethics codes, while leading workshops and interdisciplinary committees to promote responsible AI in library services.

What is the societal benefit of investing in LIS-led AI initiatives?

Such investments yield reliable, ethical AI deployment that reduces privacy breaches and biases, fosters inclusive AI literacy for communities, optimizes public services like education and access, and maximizes positive societal impacts through equitable information governance.

In Conclusion

In the rapidly evolving AI era, Library and Information Science (LIS) emerges not merely as a field that survives change, but as an essential and indispensable ethical curator, privacy guardian, and catalyst for innovation that actively shapes and nurtures trustworthy AI ecosystems.

Library and information science professionals play a pivotal role in regulating data quality, ensuring that the information feeding AI systems is accurate and reliable, while also advocating strongly for user rights and data protection. Moreover, these experts are at the forefront of pioneering new human-AI service models that integrate technology seamlessly with human needs.

Their efforts lead to tangible, measurable impacts, including the development of algorithms with significantly reduced bias, the deployment of AI systems that prioritize and secure user privacy, and the empowerment of communities to become more fluent and confident in navigating and utilizing advanced technological tools effectively.

Key Takeaways and Important Insights:

Library and information science principles play a crucial role in strengthening AI systems by protecting them against potential flaws and vulnerabilities. These principles help guarantee that AI technologies operate in a manner that is fair and transparent, promoting trust and accountability. By adhering to these guidelines, AI systems are designed to serve society equitably, ensuring that benefits are distributed justly across different communities and individuals. This approach fosters ethical development and deployment of AI, ultimately supporting social good and minimizing harm.

Ethical stewardship firmly positions libraries as essential societal safeguards, playing a critical role in mitigating various risks while simultaneously amplifying and promoting core human values. By upholding principles of integrity, fairness, and respect, libraries serve as trusted institutions that protect community interests and foster a culture of ethical responsibility. This commitment ensures that libraries not only provide access to information but also nurture the moral and social fabric of society, reinforcing their importance in safeguarding human dignity and collective well-being.

Service transformation significantly elevates the role of Library and information science by shifting it from performing routine and repetitive tasks to taking on a leadership position in advancing AI literacy and fostering collaborative intelligence across teams and departments.

For Library and information science practitioners, it is essential to actively embrace AI ethics auditing, develop expertise in prompt engineering, and foster strong interdisciplinary partnerships to effectively lead the charge in this rapidly evolving technological landscape.

Technology builders should prioritize integrating LIS expertise from the earliest stages of development to ensure the creation of robust, fair, and unbiased AI systems that serve diverse user needs. Decision-makers are encouraged to allocate funding and resources to LIS initiatives, as the return on investment in terms of risk reduction, innovation, and ethical AI deployment is both significant and undeniable.

Explore even more deeply through ALA’s extensive AI resources, participate in university symposia such as those hosted by Northwestern, or engage with innovative frameworks like AILIS 1.0. By placing LIS at the core of these efforts, we actively contribute to building a future with AI that ensures knowledge remains accessible, equitable, and focused on human values and needs.

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