Inclusive and Ethical AI for Academic Libraries

The webinar focuses on how academic libraries can ethically and inclusively adopt and integrate artificial intelligence (AI). It brings together experts to share insights on the potential and challenges of AI in library services, notably how AI can support diversity, equity, and inclusion (DEI) in higher education. The discussion also covers the broader implications of AI technologies in academic settings, including governance, accessibility, ethics, and employment impacts.

Defining Inclusive AI

Inclusive AI emphasizes developing AI systems designed to be fair, transparent, and representative of diverse groups. It is not enough for AI to be efficient; it must be created consciously to eliminate biases, especially those that reinforce historical inequities. AI systems should serve all users, including historically marginalized and underrepresented groups.

In academic libraries, inclusive AI would ensure that all students, faculty, and staff—regardless of race, gender, socioeconomic status, or ability—can access and benefit from AI-driven tools and resources. Libraries are increasingly integrating AI into their systems, and these tools must reflect the values of inclusivity.

The Role of Academic Libraries in Ethical AI

Academic libraries have a unique opportunity to lead the ethical use of AI in higher education. The presenters stressed that libraries must not just adopt AI for modernization but should focus on using AI to support ethical research and education. Libraries are historically seen as places of equitable access to information, and this mission should guide their approach to AI.

However, a key challenge lies in avoiding ethical paralysis—an overemphasis on potential harm that stifles innovation. The presenters encourage libraries to actively shape AI use by applying ethical frameworks while embracing AI’s potential to expand access and services. This means that while it's essential to be mindful of the potential ethical issues, it's equally important not to let these concerns hinder the adoption and innovation of AI in libraries.

The role of libraries extends beyond mere AI adoption. 

Libraries can champion ethical AI by Developing AI Governance Structures. Creating internal committees or teams to oversee AI development and implementation ensures that moral principles are embedded in library AI systems.

Educating the Community: Libraries should inform students and faculty about AI, not only using these tools but also their limitations and the biases they may reflect.

Ethical Auditing: Libraries can lead in auditing AI systems to check for bias, discrimination, and inequities that may arise in the data these systems use or the results they generate.

Libraries as Centers for AI Education and Skill Development

Libraries are ideal institutions for promoting AI literacy. They provide a safe and secure environment for students, faculty, and staff to learn AI tools. Presenters have pointed out that many individuals still lack confidence or skills in using AI technologies, and libraries can bridge this gap by offering training programs. This is particularly important in helping individuals understand how AI systems work and their applications in academic research. However, grasping AI's ethical implications is equally crucial, as this understanding empowers us to use AI responsibly. 

Libraries can play a crucial role in educating their communities about AI by using these tools and understanding their limitations and the biases they may reflect. AI Labs and Resources: By introducing specific AI tools such as Bard for natural language processing and ChatGPT for conversational AI, libraries provide controlled environments where students can learn to use these technologies safely and responsibly, instilling confidence in their abilities.

Upskilling Library Staff

Staff training in AI literacy is essential for libraries and other organizations to ensure employees can effectively work with AI technologies and support users in navigating AI-driven systems. Training should cover several key areas:

Understanding AI Functionality: Staff should learn how AI systems operate, including machine learning, natural language processing, and data analysis techniques. This knowledge allows them to interact with AI tools confidently, making troubleshooting issues or answering user questions easier.

Ethical Considerations: AI systems often involve ethical issues such as data privacy, bias, transparency, and the impact of AI on employment. Training should emphasize these concerns, recognizing the staff's role in responsibly guiding users through these issues. By understanding these ethical challenges, staff can ensure AI technologies are used to promote fairness and inclusivity, making them an integral part of the process.

AI as a Collaborative Tool: Rather than viewing AI as a threat to their jobs, staff should be taught how AI can complement their work, automate repetitive tasks, and allow them to focus on more complex, value-added services. For instance, AI can assist in tasks like resource curation, chatbots for customer service, or data management, while human staff can focus on user engagement and decision-making. This can lead to significant cost savings and efficiency improvements for the library.

Practical Applications: Staff training should also include practical applications of AI systems, such as using AI-driven cataloging systems or chatbots and assisting users in navigating AI-enabled services like personalized recommendations or automated research assistance. This practical knowledge will make staff feel more prepared and competent.

Addressing Bias in AI Systems

One of the major concerns discussed was the inherent bias in many AI systems. Large language models and other AI technologies often draw from existing data sources, which may reflect societal biases, particularly those rooted in colonial, Eurocentric, or otherwise exclusionary perspectives. As a result, AI systems can unintentionally perpetuate the same biases in their training data.

This is where libraries can play a significant role by Ensuring Diverse Data Sources. When training AI models, the data must come from diverse, inclusive sources representing various cultures, languages, and perspectives. This commitment to inclusivity in AI training should make the audience feel integral to creating a fair and representative AI system. Critical Use of AI Outputs: Users of AI tools in academic libraries should be encouraged to critically evaluate the results generated by AI, recognizing the possibility of biased outputs.

The presenters emphasized that libraries must educate their communities on how to interpret AI outputs and make decisions about the credibility and relevance of information, especially when using generative AI in research and learning.

AI and Accessibility

The integration of AI also brings new opportunities for improving library accessibility. AI tools such as text-to-speech, automatic transcription, and machine translation can significantly enhance access for students with disabilities or language barriers. This potential of AI to break down accessibility barriers should inspire optimism about the future of library services. However, the presenters cautioned that AI systems must be designed with accessibility in mind from the outset. Many current AI models still need to be improved in understanding diverse languages and dialects, which can be a significant limitation for inclusive access.

AI Governance and Policy in Libraries

Another key topic was the need for robust governance structures within academic libraries to manage AI technologies. The presenters suggested that libraries implement AI governance frameworks that address questions like: How do we ensure AI is aligned with our DEI goals?
How do we regularly audit AI tools for bias or inequity?
What processes are in place for user feedback on AI tools?

The Impact of AI on Library Jobs

There was also discussion about the fear that AI might replace library jobs. However, it's important to note that AI can automate specific tasks, such as cataloging, answering basic reference queries, or analyzing large datasets, freeing up library staff from repetitive tasks. This can allow them to focus on more complex, human-centered services such as personalized research assistance, instructional design, and DEI initiatives. While some routine tasks may be automated, the presenters argued that AI should be seen as an enhancement to human labor, not a replacement.

AI can free library staff from repetitive tasks, allowing them to focus on more complex, human-centered services such as personalized research assistance, instructional design, and DEI initiatives. To mitigate the fear of job displacement, the presenters suggested libraries provide ongoing training and reskilling opportunities so staff can effectively collaborate with AI tools.