Overcoming Challenges: How NPR Digitized Their Music Collection with AI

Practical Application of AI: Evaluating Music to Build a Music Library

Presented by Jane Gilvin, NPR's Research Archives and Data Team

Introduction

Jane Gilvin delivered a presentation on how her team at NPR utilized artificial intelligence (AI) to automate the identification of instrumental and vocal music to build a digital music library more efficiently. The session focused on the practical application of AI in music cataloging, the challenges faced, and the solutions implemented.

About Jane Gilvin and the RAD Team

• Jane Gilvin:
  • Member of NPR's Research Archives and Data (RAD) Team for nearly 13 years.
  • Educational background in music and library science.
  • Alumna of San Jose State University's Information Science program.
  • Experience in radio since she was a teenager.
• The RAD Team:
  • Formerly known as the NPR Library, established in the 1970s.
  • Responsible for collecting NPR programming archives.
  • Provides resources for production, including a comprehensive music collection.

NPR's Music Collection Evolution

The NPR music collection has evolved alongside technological advancements:

• Vinyl Records: The initial collection comprised vinyl records across various genres.
• Transition to CDs: Shifted to compact discs (CDs) as CD players became standard in production.
• Digital Music Files: Moved towards digital files to meet the expectations of quick and remote access to music.

Challenges in Digitizing the Collection

The transition to digital presented several challenges:

• Converting thousands of physical CDs into digital files for immediate access.
• Ensuring metadata accuracy and consistency, especially for instrumental and vocal classification.
• Lack of resources for continuous large-scale ingestion and cataloging of new music.

Solution: Automation with AI

The Robot and ORRIS

• The Robot: A batch processing system capable of ripping CDs, identifying metadata from online databases, and delivering MP3 and WAV files with embedded ID3 tags.
• ORRIS (Open Resource and Research Information System): A new database developed to allow users to search, stream, and download songs for production.

Implementing Essentia

• Essentia: An open-source library and collection of tools used to analyze audio and music to produce descriptions and synthesis.
• Capabilities: Predicts genre, beats per minute, mood, and most importantly, classifies tracks as instrumental or vocal.
• Training the Algorithm: Used NPR's extensive archive of over 300,000 tracks with existing instrumental and vocal tags to train the algorithm.

Accuracy and Testing

• Human Cataloging Accuracy: Ranged from 90% to 98%, averaging around 90% due to human error and limitations.
• Algorithm Accuracy Goal: Set at 80% to balance the usefulness and the efficiency of the process.
• Results: The algorithm achieved an accuracy of 86%, meeting the team's criteria.

Integration and Quality Control

Building into the Ingest Process

• Automated the instrumental/vocal tagging during the ingest process of new tracks.
• Applied the algorithm to existing tracks that lacked instrumental/vocal classification.

User Feedback Mechanism

• Added a feature allowing users to report incorrectly tagged songs directly from the ORRIS interface.
• Provided a quick way for the RAD team to receive notifications and correct metadata errors.

Quality Control Measures

• Automated spreadsheets generated during the algorithm's run allowed for immediate review of results.
• Periodic checks to ensure the algorithm continues to perform within the acceptable accuracy range.
• Addressed any shifts in algorithm performance due to changes in the type of music being ingested or other factors.

Demonstration

Jane provided a live demonstration of how the process works:

1. Showed the ORRIS search interface and how users can search for and listen to tracks (e.g., Thelonious Monk, David Bowie).
2. Demonstrated the ingestion of new albums and how the algorithm processes them to classify tracks as instrumental or vocal.
3. Illustrated the use of the user feedback feature to report incorrect classifications.

Benefits and Outcomes

• Significantly reduced the time and resources required for music cataloging.
• Enabled continuous addition of new music to the library despite limited staff time.
• Improved user satisfaction by providing a reliable point of data for instrumental and vocal tracks.

Challenges and Considerations

• Training Data Limitations: Ensuring the training data was representative and free from bias or errors.
• Algorithm Bias: Addressing the overrepresentation of certain genres (e.g., jazz and classical) in the training data to avoid skewed results.
• Metadata Accuracy: Dealing with inconsistent or incorrect metadata from external sources.

Future Plans

Jane discussed potential future projects:

• Revisiting other algorithms from Essentia, such as those predicting timbre and mood.
• Implementing user testing and UX projects to improve data research and user experience.
• Continuing to refine the algorithm and processes to maintain or improve accuracy.

Questions and Answers

During the Q&A session, several topics were addressed:

Copyright and Licensing Considerations

• NPR has licenses with major performing rights organizations for the use of music in production.
• Other libraries considering building a music collection should review legal permissions and terms of use.

Data Labeling and Ongoing QA/QC

• The team performs periodic quality checks but does not engage extensively in data labeling projects.
• Emphasis on monitoring algorithm performance and making adjustments as needed.

User Testing and UX Improvements

• Future plans include conducting user testing to evaluate the effectiveness of additional algorithms (e.g., mood taxonomy).
• Goal is to enhance the search and discovery experience for users.

Conclusion

Jane concluded by emphasizing how the application of AI allowed the RAD team to develop a less time-consuming ingest and cataloging process. This enabled the continuous growth of the music library, providing valuable resources to production staff while efficiently managing limited staff time.

Contact Information

For further information or inquiries, you can reach out to Jane Gilvin through NPR's Research Archives and Data Team.

Protecting Your Privacy: The Risks of Sharing Sensitive Data with AI Tools

Deliberately Safeguarding Privacy and Confidentiality in the Era of Generative AI

Presented by Reed N. Hedges, Digital Initiatives Librarian at the College of Southern Idaho

Introduction

Reed N. Hedges delivered a presentation focusing on the critical importance of safeguarding privacy and confidentiality when using generative artificial intelligence (AI) tools. The session highlighted the potential risks associated with sharing sensitive data with AI models and provided actionable recommendations for users and professionals in the library and information science fields.

Personal Anecdotes and the Need for Caution

Hedges began by sharing several personal anecdotes illustrating how individuals unknowingly compromise their privacy by inputting sensitive information into AI tools:

• A user who spends long hours chatting with GPT-4, sharing more personal information with the AI than with their own spouse.
• An individual who input all their grandchildren's data into an AI to generate gift ideas.
• A person who provided detailed demographic data of a local social group, including identifiable information, to plan activities and programs.
• A user who entered their entire family budget into an AI tool for financial management.

These examples underscore the pressing need for users to be more conscientious about the data they share with AI systems.

Main Point: Do Not Input Sensitive Data into AI Tools

The core message of the presentation is clear: Users should not input any sensitive or personal data into prompts for generative AI tools. This includes business information, personal identifiers, or any data that could compromise individual or organizational privacy.

Privacy Policies and Data Handling by AI Tools

Hedges highlighted specific concerns regarding popular AI tools:

• Google Bard: Explicitly notes that human supervisors may read user data, emphasizing the importance of anonymization.
• OpenAI's ChatGPT: Terms of use discuss the need for proprietary data protection. Users can have a more privacy-conscious session by using OpenAI's Playground or adjusting settings at privacy.openai.com/policies.
• Perplexity AI: Evades questions about data handling and extrapolation.

The Challenge of Legal Recourse and Privacy Harms

The presentation delved into the limitations of current privacy laws:

• Harm Requirement: Courts often require proof of harm, which is challenging when privacy violations involve intangible injuries like anxiety or frustration.
• Impediments to Enforcement: The need to establish harm impedes the effective enforcement of privacy violations, allowing wrongdoers to escape accountability.
• Lack of Adequate Legal Framework: The existing legal system lacks effective mechanisms to address privacy harms resulting from AI data handling.

Extrapolation and Inference by AI Tools

Generative AI models can infer additional information beyond what users explicitly provide:

• Data Extrapolation: AI tools can infer behaviors, engagement patterns, and personal attributes from minimal data inputs.
• Privacy Risks: Such extrapolation can inadvertently reveal sensitive information, including learning disabilities or mental health issues.
• Example: Even generic prompts can lead to AI inferring personal details that compromise privacy.

Recommendations for Safeguarding Privacy

1. Transparency in Data Collection

• Inform users about the data being collected and its intended use.
• Only OpenAI's ChatGPT and Anthropic's Claude explicitly deny storing and extrapolating user data.

2. Informed Consent

• Obtain explicit consent before collecting or using personal information.
• Ensure users are aware of the implications of data sharing with AI tools.

3. Data Minimization

• Limit data collection to what is absolutely essential for the task.
• Avoid including unnecessary personal or demographic details in AI prompts.

4. Anonymization and Avoiding Sensitive Information

• Do not include individual attributes or identifiers in AI prompts.
• Use synthetic or generalized data where possible.
• Be cautious even with public data, as ethical considerations remain.

5. Implement Strict Access and Use Controls

• Enforce a "least privilege" access model, using tools that require minimal data access.
• Ensure staff and users are clear on what data can be input into AI tools.

6. Use Human Content Moderation

• Have prompts reviewed by multiple individuals to screen for privacy issues.
• This process can also enhance quality control.

7. Be Skeptical of "Secure" AI Tools

• Avoid promising or assuming that any AI tool is completely secure.
• Recognize that even custom AI models can be vulnerable to exploitation.

Understanding AI Terms of Service

Users should familiarize themselves with the terms of service of AI tools:

• Ownership of Content: OpenAI states that users own the input and, to the extent permitted by law, the output generated.
• Responsibility for Data: Users are responsible for ensuring that their content does not violate any laws or terms.
• Data Use: AI providers may use input data for training and improving models unless users opt out.

Final Thoughts on Privacy Practices

Hedges emphasized that traditional privacy protection principles remain relevant but must be applied more diligently in the context of AI:

• Extra Vigilance: Users must be proactive in safeguarding their data when interacting with AI tools.
• Data Breaches are Inevitable: Even with safeguards, data breaches can occur; therefore, minimizing shared data is crucial.
• Reassessing the Need for AI: Consider whether using AI is necessary for a given task, especially when handling sensitive information.

Conclusion

In the era of generative AI, safeguarding privacy and confidentiality requires deliberate and informed actions by users and professionals. By understanding the risks, adhering to best practices, and educating others, individuals can mitigate potential harms associated with AI data handling.

References and Further Reading

• Danielle Keats Citron and Daniel J. Solove: "Privacy Harms" - A comprehensive paper discussing the challenges in addressing privacy violations legally.
• Shantanu Sharma: "Artificial Intelligence and Privacy" - An exploration of AI's impact on privacy, available on SSRN.
• Nathan Hunter: "The Art of ChatGPT Prompting: A Guide to Crafting Clear and Effective Prompts" - A book providing insights into effective AI interactions.

Links to these resources were provided during the presentation for attendees interested in deepening their understanding of AI privacy concerns.

ridging the Gap: The Role of Librarians in Facilitating AI Integration in Library Instruction

Faculty Attitudes Toward Librarians Introducing AI in Library Instruction Sessions

Presented by Beth Evans, Associate Professor at Brooklyn College, City University of New York

Introduction

Beth Evans delivered a presentation discussing the role of librarians in introducing artificial intelligence (AI) tools in library instruction sessions. With over 30 years of experience at Brooklyn College's library, she explored faculty perspectives on the use of AI in academic settings and the potential implications for library instruction.

Background

Evans noted that AI technologies like ChatGPT have the potential to augment, support, or even replace certain library functions, such as reference services, instruction, and technical services. Recognizing the transformative impact of AI, she sought to understand faculty attitudes toward AI and whether they would welcome librarians incorporating AI tools into their instruction sessions.

Research Methodology

In the fall of 2023, Evans conducted a survey targeting faculty members at Brooklyn College. Key aspects of the survey included:

• Distributed to 199 faculty members.
• Received 74 responses, representing a response rate of approximately 37%.
• Respondents came from various departments, with the largest representation from English, History, and Sociology.
• Questions focused on faculty's introduction of AI in their courses, their attitudes toward AI, and their openness to librarians discussing AI in instruction sessions.

Survey Findings

Faculty Introduction of AI in Courses

Evans explored how faculty members addressed AI in their teaching:

• Proactive Introduction: Some faculty included AI tools in their syllabi, assignments, or class discussions.
• Student-Initiated Discussions: In a few cases, students brought up AI topics during classes.
• No Introduction: A portion of faculty did not introduce AI topics at all.

Methods of Introducing AI

Among faculty who addressed AI:

• Rule Setting in Syllabi: Establishing guidelines on AI usage in course policies.
• Class Discussions: Engaging students in conversations about AI's role and impact.
• Assignments Involving AI: Incorporating AI tools as part of coursework to critically evaluate their utility.

Faculty Attitudes Toward AI

Faculty responses reflected a spectrum of attitudes:

1. Prohibitive

Some faculty strictly prohibited the use of AI tools, expressing concerns about academic integrity and potential threats to human creativity and critical thinking.

2. Cautionary

Others cautioned students about relying on AI, highlighting limitations and encouraging transparency if AI tools were used.

3. Preventative

Certain faculty designed assignments that were difficult or impossible to complete using AI tools, thereby discouraging their use.

4. Proactive Utilization

A group of faculty embraced AI, integrating it into their teaching to enhance learning outcomes:

• Using AI for media literacy discussions.
• Employing AI to improve cover letters in business courses.
• Assigning comparative analyses between AI-generated content and traditional research tools like PubMed.

Faculty Concerns About Librarians Introducing AI

When asked whether they were concerned about librarians introducing AI in library instruction sessions:

• Majority Not Concerned: Most faculty members were open to librarians discussing AI tools.
• Supportive of Librarian Expertise: Many acknowledged librarians as information experts capable of providing balanced and ethical guidance on AI.
• Strong Opposition: A minority expressed strong opposition, fearing AI as a threat to human flourishing and academic integrity.

Additional Faculty Comments

Faculty provided further insights:

Ambivalence and Hesitation

• Some were uncertain about AI's role and expressed a need for more understanding before fully integrating it.
• Concerns about keeping pace with rapidly evolving technology and its implications for cheating and academic dishonesty.

Recognizing the Inevitable Presence of AI

• Acknowledgment that AI is prevalent and students need to be educated about its use.
• Emphasis on not burying heads in the sand and preparing students for real-world applications where AI is utilized.

Desire for Collaboration with Librarians

• Faculty expressed interest in workshops and collaborations led by librarians to explore AI tools constructively.
• Appreciation for librarians' efforts to assist both students and faculty in understanding AI's prevalence and uses.

Conclusion

Beth Evans concluded that while faculty attitudes toward AI vary widely, there is significant openness and even enthusiasm for librarians to take an active role in introducing and educating about AI tools in library instruction sessions. Librarians are viewed as information experts well-equipped to navigate the ethical, practical, and pedagogical aspects of AI in academic settings.

Implications for Librarians

Based on the survey findings:

• Librarians have an opportunity to lead in AI literacy education, providing balanced perspectives on AI tools.
• Collaboration with faculty is essential to ensure that AI integration aligns with course objectives and academic integrity policies.
• There is a need to address concerns and misconceptions about AI, tailoring approaches to different disciplines and faculty attitudes.

Contact Information

For further information or collaboration opportunities, you can contact Beth Evans:

• Email: bevans@brooklyn.cuny.edu
• Affiliation: Brooklyn College Library, City University of New York

Note: The final slide of the presentation included an AI-generated image using the tool "Tome" with the theme "Ocean."

Navigating the Intersection of AI and Information Literacy: Essential Competencies for Librarians

Competencies for the Use of Generative AI in Information Literacy Instruction

Presented by Paul Pival, Librarian at the University of Calgary

Introduction

During the Library 2.0 Mini-Conference on AI and Libraries, Paul Pival delivered a presentation titled "Competencies for the Use of Generative AI in Information Literacy Instruction." The session focused on identifying the essential competencies that librarians should possess to effectively incorporate generative artificial intelligence (AI) into information literacy instruction.

Frameworks vs. Competencies

Paul began by distinguishing between frameworks and competencies. While frameworks serve as blueprints outlining how various components fit together (analogous to building a house), competencies are the specific skills and knowledge required to execute those plans (the materials needed to build the house).

He referenced the Association of College and Research Libraries (ACRL) Framework for Information Literacy for Higher Education, noting that it is broad enough to encompass generative AI. He highlighted that efforts are underway, led by professionals like Dr. Leo Lo, to update the framework to explicitly address generative AI.

ACRL Framework and Generative AI

Paul discussed how the six frames of the ACRL Framework relate to generative AI:

1. Authority is Constructed and Contextual: Emphasizing the importance of assessing content critically and acknowledging personal biases when evaluating AI-generated information.
2. Information Creation as a Process: Understanding how large language models (LLMs) generate content and accepting the ambiguity in emerging information formats.
3. Information Has Value: Recognizing the need to cite AI-generated content appropriately and verifying the accuracy of AI-provided citations.
4. Research as Inquiry: Utilizing AI tools to break down complex problems and enhance inquiry-based learning.
5. Scholarship as Conversation: Engaging in dialogues with AI tools, understanding that they are conversational agents rather than traditional search engines.
6. Searching as Strategic Exploration: Acknowledging that searching is iterative and that AI tools complement but do not replace traditional academic databases.

Essential Competencies for Librarians

Paul proposed four key competencies that librarians should develop to effectively use generative AI in information literacy instruction:

1. Understanding How Generative AI Works:
   • Familiarity with the leading AI models, referred to as "Frontier Models," including GPT-4, Google's Gemini 1.0, and Anthropic's Claude 3.
   • Investing time (at least 10 hours per model) to become proficient with their nuances.
   • Recognizing accessibility issues, such as subscription costs and geographical restrictions, which contribute to the digital divide.
2. Recognizing Bias in AI Models:
   • Understanding that AI models are trained on vast internet data, including biased and harmful content.
   • Acknowledging that the programming and training data may not represent diverse worldviews.
   • Being aware of potential overcorrections and content filtering issues.
3. Identifying and Managing Hallucinations:
   • Recognizing that AI models may generate false or fabricated information, including non-existent citations.
   • Understanding the concept of "hallucinations" in AI and their implications for information accuracy.
   • Exploring solutions like Retrieval Augmented Generation (RAG) to mitigate hallucinations by incorporating domain-specific knowledge bases.
4. Ethical Considerations:
   • Evaluating the ethical implications of using AI tools, including environmental impacts and labor practices.
   • Understanding legal issues related to copyright and content usage.
   • Considering the potential for AI tools to disseminate disinformation.

Resources and Continuous Learning

Paul emphasized the importance of continuous learning and adaptability in AI literacy. He provided several resources for further exploration:

• ACRL Framework for Information Literacy Toolkit
• ACRL Framework for Information Literacy Sandbox
• "Reimagining Information Literacy Instruction: Framework for the Future" by Amy James and Ellen Filgo
• ACRL's Generative AI Task Force

Conclusion

In conclusion, Paul highlighted that AI literacy is not static but evolves with technological advancements. He urged librarians to:

• Educate themselves on generative AI tools and their implications.
• Integrate AI competencies within existing information literacy frameworks.
• Stay informed about ethical considerations and emerging issues.
• Promote continuous learning to adapt to the rapidly changing AI landscape.

By developing these competencies, librarians can better serve their patrons and help navigate the complexities introduced by generative AI in information literacy instruction.

Contact Information

You can connect with Paul Pival on social media platforms under the handle @ppival.

AI in Libraries: Unlocking the Potential for Public Libraries

AI and Libraries: Applications, Implications, and Possibilities

Opening Keynote at the Library 2.0 Mini-Conference

Introduction

The Library 2.0 mini-conference titled "AI and Libraries: Applications, Implications, and Possibilities" was held, featuring an opening keynote panel discussion. The conference was organized by San Jose State University's School of Information, with special thanks extended to Dr. Sandra Hirsh and Dr. Anthony Chow for their leadership. The keynote was moderated by Dr. Raymond Pun, an academic and research librarian at Alder Graduate School of Education and a prominent figure in the field.

Panelists

The panel consisted of esteemed professionals from various library settings:

• Ida Mae Craddock: School Librarian at Albemarle County Public Schools' Community Lab Schools in Virginia.
• Dr. Brandy McNeil: Deputy Director of Programs and Services at the New York Public Library.
• Dr. Leo Lo: Dean and Professor of the College of University Libraries and Learning Sciences at the University of New Mexico.

AI in Different Library Contexts

Public Libraries

Dr. Brandy McNeil discussed how public libraries are integrating AI to enhance both internal and external operations. Key applications include:

• Automating FAQs and email responses.
• Assisting with customer complaints and inquiries.
• Creating curriculum outlines and scheduling.
• Cataloging books and ensuring data accuracy.
• Offering information literacy classes on AI basics.

She highlighted the establishment of an AI committee at the New York Public Library, modeled after the Library of Congress's phases of AI—understanding, experimenting, and implementing. The committee explores AI tools like Whisper AI and the Devon software (an AI software engineer), and collaborates with institutions like the Library of Congress.

School Libraries

Ida Mae Craddock shared insights from the school library perspective, noting that school librarians are often the first to encounter and integrate new technologies. AI is being used for:

• Generating essays and leveling texts to match student reading levels.
• Translating materials to make curriculum accessible to non-native English speakers.
• Creating custom educational materials quickly.
• Processing data and scheduling.

She emphasized the importance of policies guiding AI use in schools, particularly regarding student data privacy and compliance with laws like FERPA.

Academic Libraries

Dr. Leo Lo discussed the exploration of AI in academic libraries, particularly generative AI. The University of New Mexico initiated a GPT-4 exploration program involving staff from different units with varying levels of AI expertise. Applications included:

• Generating alt text for images and editing bibliographies.
• Developing machine-readable data management plans.
• Facilitating staff-patron interactions using AI-generated templates and FAQs.
• Using AI for cataloging and metadata management.
• Assisting with administrative tasks like scheduling and email drafting.

Dr. Lo emphasized the importance of experimenting with AI to discover its potential benefits and limitations within the academic library context.

Popular AI Tools and Applications

The panelists discussed various AI tools being utilized in their respective settings:

Tools in Public Libraries

• ChatGPT: Used for a variety of tasks, with some staff using the paid version for advanced features.
• Canva Magic Studio: For creating promotional materials and program flyers.
• Midjourney and Stable Diffusion: Image generation tools.
• Microsoft Co-Pilot and Google's Duet AI: For productivity and note-taking features.
• Otter AI: For transcription and translation services.
• Quick Draw by Google and Goblin Tools: For educational demonstrations of AI capabilities.
• Adobe Firefly and Character.ai: For creative and interactive experiences.

Tools in School Libraries

• ChatGPT: For natural language processing tasks and assisting students in generating research topics.
• BigHugeLabs Image Editor: For easy image editing tasks.
• Diffit: For leveling texts and generating practice questions aligned with testing cultures in schools.
• Google Immersive Translate and Rask AI: For translating materials to support multilingual students.
• OpenAI Codex and TabNine: For coding and creating custom AI models to process specific data.

Tools in Academic Libraries

• ChatGPT and GPT-4: For various research and administrative tasks.
• Claude from Anthropic and Google Bard: Alternative AI models for exploration.
• Perplexity AI: A tool that could potentially change information discovery processes.
• Scite.ai and Kendra: Research-oriented models for academic purposes.
• Elsevier's Scopus AI: An AI developed by publishers to assist with academic research.

Concerns and Ethical Considerations

Policy and Privacy Issues

The panelists emphasized the importance of policies guiding AI use, especially concerning data privacy, equity, and access. Key points included:

• Ensuring student data privacy in compliance with laws like FERPA.
• Addressing the digital divide and information privilege associated with access to AI tools.
• The need for clear institutional policies to guide AI use in educational settings.

Copyright and Intellectual Property

The discussion highlighted significant concerns regarding AI's impact on copyright and intellectual property:

• Ongoing lawsuits against AI companies for copyright infringement and the use of copyrighted materials in training data.
• The complexity of citing AI-generated content and the ethical implications of using AI outputs in academic work.
• The need for balanced approaches to protect creators' rights while allowing AI to be used for research and educational purposes.

Bias, Equity, and Labor Practices

Other concerns included:

• Biases present in AI models due to the data they are trained on, affecting marginalized communities.
• Environmental impacts of large data centers required for AI processing.
• Labor practices related to content moderation and the underpaid workforce behind AI technologies.

Resources and Staying Informed

The panelists shared various resources for librarians and professionals to stay updated on AI developments:

• Attending conferences and workshops, such as those hosted by the Public Library Association and the American Library Association.
• Following technology news outlets like The Verge, Mashable, Wired, CNET, and MIT Technology Review.
• Engaging with local tech platforms and staying informed about funding opportunities and industry trends.
• Reading reports from organizations like the Pew Research Center and the Center for an Urban Future.
• Following thought leaders and experts in the field on social media and professional networks.
• Utilizing library-specific publications like School Library Journal and Knowledge Quest.
• Listening to relevant podcasts and webinars, such as those offered by Choice 360 and New York Times' "Hard Fork."

Impact on Library Workforce and Future Outlook

The panelists concluded with reflections on how AI might impact the library workforce:

• Ida Mae Craddock expressed optimism that AI would not replace school librarians but would change certain aspects of the job, emphasizing the irreplaceable role of librarians in teaching critical thinking and fostering a love of reading.
• Dr. Leo Lo highlighted the importance of upskilling and reskilling, suggesting that AI would change job functions rather than eliminate positions. He mentioned efforts to develop AI competencies for library workers through organizations like ACRL.
• Dr. Brandy McNeil noted that while AI might not replace people, it could replace those who do not know how to use it effectively. She emphasized the emergence of new job roles like prompt engineering and the need for library professionals to adapt.

Conclusion

The opening keynote of the Library 2.0 mini-conference provided valuable insights into the current state and future possibilities of AI in various library contexts. The panelists highlighted both the practical applications and the ethical considerations that come with integrating AI into library services. Key takeaways include:

• The transformative potential of AI to enhance library operations, accessibility, and user engagement.
• The critical importance of policies, ethical considerations, and ongoing dialogue to navigate challenges related to privacy, equity, and intellectual property.
• The need for library professionals to stay informed, adapt to new technologies, and continue their role as educators and facilitators in an evolving information landscape.

The conference emphasized that while AI presents significant opportunities for innovation, it also requires thoughtful implementation and a commitment to addressing its broader societal impacts.

Additional Information

The panelists encouraged attendees to participate in upcoming sessions of the mini-conference and to engage with resources and networks to further explore AI's role in libraries.

Revolutionizing Research: A Look at AI and Data Innovations in Higher Education

New AI and Data Innovations in the Classroom: A Roundtable Discussion

Presented by Miraj Berry, Brian Cooper, Josh Nicholson, and Joe Karaganis

Introduction

The Charleston Library Conference hosted a virtual roundtable discussion titled "New AI and Data Innovations in the Classroom". The session brought together experts in the field of educational technology to discuss the application and usefulness of AI tools and databases in higher education settings. The panelists included:

• Miraj Berry: Director of Business Development at Overton.
• Brian Cooper: Associate Dean of Innovation and Learning at Florida International University (FIU) Libraries.
• Josh Nicholson: Founder and CEO of Scite.
• Joe Karaganis: Director of Open Syllabus.

The 30-minute session, followed by a 10-minute live Q&A, aimed to explore the use cases of three innovative tools—Overton, Scite, and Open Syllabus—and their impact on teaching, learning, and library services in higher education. The discussion focused on how these tools leverage AI and large data sets to enhance content discovery, support classroom instruction, and contribute to textbook affordability and collection development initiatives.

Overview of the Tools

Overton

Overton is the world's largest database of policy documents and grey literature. It indexes over 9.3 million policy documents from more than 1,800 sources across 32,000 organizations in 188 countries. The platform makes policy documents easily searchable and discoverable by indexing their full text and linking them to academic papers, relevant people, topics, and Sustainable Development Goals (SDGs).

Miraj explained that Overton's mission is to support evidence-based policymaking by providing a platform that allows users to explore the connections between policy documents and scholarly research. Overton helps surface content that might otherwise be difficult to find, putting existing content into perspective for researchers, students, and policymakers.

Open Syllabus

Open Syllabus is an open-source syllabus archive that collects and analyzes millions of syllabi from around the world. With a database of around 20 million syllabi, the platform uses AI and machine learning to extract structured information from these documents, such as course descriptions, reading lists, and learning outcomes.

Joe highlighted that Open Syllabus aims to make the intellectual backdrop of teaching more accessible. By aggregating syllabi at scale, the platform provides insights into what is being taught, how subjects are structured, and which materials are considered central or peripheral in various fields. This information can inform curricular design, collection development, and OER (Open Educational Resources) adoption initiatives.

Scite

Scite is an AI-powered platform designed to help users better understand and evaluate research articles. By leveraging machine learning, Scite processes millions of full-text PDFs to extract citation statements, providing context on how and why an article, researcher, journal, or university has been cited.

Josh explained that Scite addresses challenges related to information overload and trust in scholarly communication. The platform offers a "next-generation citation index" that brings more nuance and context to citations, enabling users to discover, trust, evaluate, and use research more effectively. Scite also integrates with large language models to provide fact-checking and grounding against the scientific literature.

Challenges and Opportunities in Adopting AI Tools

Critical Evaluation and Adoption

The panelists discussed the importance of critically evaluating AI tools before adopting them in educational settings. Josh emphasized that while large language models like ChatGPT offer powerful capabilities, they can also produce untrustworthy or fabricated information. Therefore, it's crucial to implement guardrails, such as providing citations and allowing users to verify sources.

Joe added that the barriers to textual analysis have significantly decreased due to advancements in AI and machine learning. This democratization means that specialized capabilities are now accessible to a broader audience, but it also raises questions about data aggregation, ethical considerations, and the responsible use of AI in education.

Supporting Staff and Students

Brian shared insights from the librarian's perspective, highlighting the challenges and initiatives at FIU in supporting textbook affordability and collection development. He noted that librarians play a neutral role in fostering AI literacy among students and faculty. By creating resources like LibGuides and engaging with faculty liaisons, libraries can help navigate the complexities of AI and digital tools.

The panelists agreed that it's essential to provide advice, training, and support for staff and student consumption of these tools. This includes understanding where these technologies might be useful, testing them, and finding possible ways to package them for educational purposes.

Feedback Channels and Collaboration

Effective adoption of AI tools requires collaboration among various stakeholders, including students, teachers, librarians, and technology vendors. The panelists discussed the importance of establishing feedback channels to gather input from users and to refine the tools based on real-world needs.

Josh mentioned that libraries have a critical role in guiding researchers and students through the suite of available tools, helping them understand the strengths and limitations of each. By being proactive and embracing these technologies, libraries can better support their communities in an era of rapid technological change.

Use Cases and Impact

Overton's Application in Policy Research

Miraj highlighted how Overton supports evidence-based policymaking by making grey literature and policy documents more accessible. Researchers and students can discover policy documents related to their field of study, explore citations between policy and academic literature, and gain a broader understanding of the policy landscape.

This accessibility enables users to incorporate policy perspectives into their research and teaching, fostering a more interdisciplinary approach to education.

Open Syllabus and Curriculum Development

Joe discussed how Open Syllabus aids in curriculum development and OER adoption. By analyzing syllabi at scale, the platform can identify commonly assigned materials, trends in subject matter, and gaps in available resources. This information can inform collection development decisions and help educators select materials that align with their instructional goals.

Brian shared that FIU is leveraging Open Syllabus to map out peer-reviewed OER materials aligned with classes nationwide. By correlating these with existing classes at FIU, faculty can be informed about OER options that their peers are using, promoting textbook affordability and enhancing student success.

Scite's Role in Research and Education

Josh explained that Scite helps address the challenges of information overload and the need for trustworthy sources. By providing context to citations and integrating with large language models, Scite allows users to fact-check information and understand the credibility of sources more effectively.

In educational settings, Scite can assist students in starting quality research papers by guiding them to relevant and reliable sources, thereby enhancing the research and learning process.

The Role of Libraries and Vendors

Libraries as Facilitators

Brian emphasized that libraries are in a unique position to bridge the gap between technology and users. By engaging in new and novel ways with their constituencies, libraries can support the adoption of AI tools, promote AI literacy, and contribute to student and faculty success.

He highlighted the potential for libraries to expand their involvement in areas like institutional effectiveness and accreditation by leveraging data and insights from tools like Open Syllabus and Scite.

Vendor Collaboration

The panelists agreed that collaboration between libraries and vendors is essential for maximizing the benefits of AI tools. Vendors can support libraries by providing data, integrating with existing systems, and offering solutions that address specific institutional needs.

Miraj mentioned Overton's commitment to being a responsible data provider, focusing on ethical considerations and user needs. Josh added that understanding how these tools can be used responsibly and developing training materials are critical steps in ensuring their effective adoption.

Conclusion

The roundtable discussion highlighted the transformative potential of AI and data innovations in the classroom and library services. By leveraging tools like Overton, Open Syllabus, and Scite, educational institutions can enhance teaching and learning experiences, support evidence-based research, promote textbook affordability, and foster AI literacy among students and faculty.

The panelists underscored the importance of critically evaluating these tools, providing support and training, and fostering collaboration among stakeholders. Libraries, in particular, have a pivotal role in guiding the adoption of AI technologies and ensuring they are used ethically and effectively.

As the landscape of educational technology continues to evolve, ongoing dialogue and partnerships will be crucial in addressing challenges and harnessing opportunities to improve education in the digital age.

Contact Information

• Overton: Visit overton.io or contact Miraj at miraj@overton.io.
• Open Syllabus: Learn more at opensyllabus.org.
• Scite: Explore Scite at scite.ai.
• Florida International University Libraries: Visit library.fiu.edu.

From Theory to Practice: How Educators are Using Packback to Boost Student Engagement

Instructional AI: A Master Class in Packback Adoption and Integration

Presented by Devon McGuire and Juliet Rogers

Introduction

In a recent webinar titled "Instructional AI: A Master Class in Packback Adoption and Integration", educators Devon McGuire and Juliet Rogers shared valuable insights into the integration of instructional AI tools in the classroom. The webinar aimed to guide teachers on effectively adopting Packback, an AI-powered platform designed to enhance student engagement, critical thinking, and writing skills.

Devon McGuire, the Director of Academic Innovation and Strategy at Packback, brought her extensive experience in educational technology to the session. Juliet Rogers, a veteran teacher with 24 years of experience at Pasadena Independent School District, provided a practical perspective based on her firsthand experience using Packback in her 10th-grade AVID (Advancement Via Individual Determination) elective class.

Understanding Instructional AI and Packback

Instructional AI refers to artificial intelligence tools specifically designed to augment the teaching and learning process. Unlike general AI applications, instructional AI focuses on enhancing the educational environment by supporting both instructors and students. Packback is one such platform that leverages AI to foster critical thinking and improve writing skills among students.

Devon explained that Packback has been utilizing AI since 2017, initially in higher education. Recognizing the opportunity to support students in developing college and career readiness skills, Packback formed a partnership with AVID, aligning with their mission to promote inquiry-based learning and student engagement.

Juliet Rogers' Journey with Packback

Juliet shared her motivation for incorporating Packback into her classroom. Despite her extensive teaching experience, she noticed that her students were struggling with writing, a critical skill for college success. She wanted a tool that could assist her students without turning her AVID elective into an English class.

After learning about Packback during an AVID training session, Juliet was eager to implement it. She appreciated that Packback didn't just correct student mistakes but also provided explanations, helping students learn from their errors. The platform's ability to handle tedious tasks like grading grammar, mechanics, and formatting freed Juliet to focus on more impactful teaching activities.

Implementing Packback in the Classroom

Weekly Routine and Integration with AVID Strategies

Juliet described how she integrated Packback into her weekly teaching routine. Every day, she began with a warm-up activity, and several times a week, this included a Packback assignment. The platform operates on a two-week rotation, allowing students ample time to engage with the material.

At the start of each week, students conducted a "weekly check-in" where they reviewed their grades and identified areas where they were struggling. Using this reflection, they crafted open-ended questions related to their core or college classes, aligning with AVID's Tutorial Request Form (TRF) process.

Juliet emphasized that the questions had to meet certain criteria, such as being open-ended and reaching a minimum "Curiosity Score" provided by Packback's AI. Students were also encouraged to include SAT vocabulary words in their posts, reinforcing their language skills.

Engaging in Inquiry-Based Discussions

Throughout the two-week period, students were required to respond to at least two of their peers' questions. Juliet guided them to choose classmates who hadn't received responses yet, promoting inclusivity and ensuring that all students received support.

The Packback platform's design encouraged students to engage in higher-order thinking, as they had to formulate thoughtful questions and provide substantive responses. This practice mirrored the collaborative and inquiry-based learning emphasized in AVID's strategies.

Leveraging Packback's Features

Curiosity Score and Leaderboard

The Curiosity Score is an AI-generated metric that evaluates the quality of student posts based on criteria like open-endedness, academic tone, and the use of credible sources. Juliet set a minimum Curiosity Score of 40 to encourage students to meet certain standards.

The Leaderboard feature fostered a friendly competition among students, motivating them to improve their scores and engage more deeply with the content. Juliet noted that students became excited about their progress and the recognition they received.

Deep Dives for Writing Practice

In addition to the regular discussions, Juliet utilized Packback's "Deep Dives" feature for more extensive writing assignments. This tool allowed her to create custom rubrics focusing on aspects like word count, grammar, formatting, and flow. She could set specific requirements, such as using APA or MLA citation styles, which helped students prepare for college-level writing.

Juliet shared an example of a student whose writing significantly improved over time, demonstrating the effectiveness of the Deep Dives in enhancing students' writing skills. The AI provided detailed feedback, highlighting areas for improvement and guiding students through revisions.

AI-Powered Feedback and Plagiarism Detection

Packback's AI not only graded assignments but also provided constructive feedback. It pointed out issues like repetitive language or formatting errors, allowing students to learn and correct mistakes independently.

The platform also included plagiarism and AI content detection features. If a student's submission appeared to be copied or generated by AI, Packback would alert the student privately, giving them an opportunity to revise their work. This approach promoted academic integrity while educating students about ethical writing practices.

Impact on Students and Learning Outcomes

Improved Writing Skills and Confidence

Juliet observed that her students' writing abilities improved noticeably over time. The regular practice and immediate feedback helped them develop stronger grammar, vocabulary, and critical thinking skills. Students began to produce more substantive and thoughtful responses, moving beyond superficial answers.

Preparation for College Expectations

The use of Packback familiarized students with discussion boards, a common component in college courses. Juliet's students, including alumni who returned to share their experiences, reported that they felt more prepared and confident in their college classes due to their practice with Packback.

One former student expressed that Packback "saved me" in college, highlighting the platform's role in easing the transition to higher education's writing and discussion demands.

Challenges and Student Feedback

While there was an initial learning curve and some resistance from students who were unaccustomed to the platform, over time, they recognized its benefits. Juliet noted that teenagers might grumble about extra work, but the growth in their skills and confidence eventually led to appreciation for the tool.

She also addressed the prevalent issue of academic dishonesty in the digital age. By integrating Packback, she provided a structured environment that discouraged cheating and emphasized the importance of original thought and effort.

Advice for Educators Considering Packback

Juliet encouraged other educators to embrace instructional AI tools like Packback, emphasizing the support and resources available. She highlighted the importance of setting clear expectations, integrating the platform into regular routines, and using it to reinforce existing instructional strategies like AVID's WICOR (Writing, Inquiry, Collaboration, Organization, Reading) framework.

Devon added that educators interested in adopting Packback should attend general training sessions to understand the foundational aspects and ensure they have the necessary district approvals, especially regarding data privacy agreements.

Conclusion

The webinar showcased how instructional AI, when thoughtfully integrated into the classroom, can significantly enhance student engagement, writing proficiency, and readiness for college-level work. Juliet's practical application of Packback in her AVID elective class provided a roadmap for other educators seeking to leverage technology to support their teaching goals.

By focusing on critical thinking, inquiry-based learning, and ethical practices, tools like Packback can play a crucial role in preparing students for the demands of higher education and beyond.

Resources and Next Steps

• Training Sessions: Educators can access recordings and training sessions at packback.co/webinars to familiarize themselves with the platform.
• Support Contacts: For assistance and onboarding, teachers can reach out to Packback representatives like Madison Shay.
• District Approval: Ensure that district consent and data privacy agreements are secured before implementation to protect student information.
• Integration with Curriculum: Align Packback activities with existing curricular frameworks like AVID to maximize effectiveness and reinforce learning objectives.

By embracing instructional AI, educators can provide their students with the tools and skills necessary to succeed in an increasingly digital and interconnected world.

Exploring the Intersection of AI and Archives: Key Insights from Experts

Opening Keynote: AI and Archives – Applications, Implications, and Possibilities

Presented by Ray Pun, Helen Wong Smith, and Thomas Padilla at the AI and Libraries 2 Mini Conference

Introduction

In the opening keynote of the "AI and Libraries 2: More Applications, Implications, and Possibilities" mini conference, Ray Pun welcomed attendees to an engaging session focused on the intersection of artificial intelligence (AI) and archives. The event built upon the previous month's conference, which saw over 16,000 sign-ups, and celebrated Ray Pun's recent election as President-Elect of the American Library Association (ALA).

Joining Ray Pun were two distinguished professionals in the field of archives:

• Helen Wong Smith: President of the Society of American Archivists (SAA) and Archivist for University Records at the University of Hawaii at Mānoa.
• Thomas Padilla: Deputy Director of Archiving and Data Services at the Internet Archive.

The session aimed to explore the current landscape of AI in archives, discuss ethical considerations, and examine how AI can make born-digital collections more accessible and usable.

The Professional Landscape of AI in Archives

Helen Wong Smith's Perspective

Helen emphasized that AI integration into the archival profession offers promising opportunities for enhancing efficiency, accessibility, and deriving new insights from archival collections. However, she also highlighted several challenges that require careful management:

• Quality and Ethics: Ensuring that AI-generated metadata and content maintain the authenticity and trustworthiness of archival records.
• Privacy: Navigating data privacy concerns when implementing AI technologies.
• Professional Adaptation: The need for ongoing dialogue, research, and training to effectively integrate AI while preserving the nature of archival records.

Helen introduced the concept of "paradata," which involves capturing information about the procedures, tools, and individuals involved in creating and processing information resources. She stressed that paradata is essential for maintaining the authenticity, reliability, and usability of records in the context of AI-generated content.

Thomas Padilla's Perspective

Thomas noted the significant engagement of senior leadership in exploring the potential of AI within libraries and archives. He mentioned initiatives like the ARL (Association of Research Libraries) and CNI (Coalition for Networked Information) Task Force focused on scenario planning for AI in these fields.

However, Thomas expressed concerns about the for-profit capture of library and archival work, cautioning against over-reliance on specific products or proprietary technologies. He emphasized the importance of a more holistic and less product-centric approach to AI integration, suggesting that focusing on overarching frameworks and values would be more beneficial for the profession.

Ethical Considerations in AI and Archival Processing

Thomas Padilla's Insights

Thomas highlighted the ethical complexities that arise when AI perpetuates existing societal biases, referencing Safiya Noble's work on "Algorithms of Oppression." He argued that it's insufficient to accept these biases as inevitable and stressed the need for proactive responses to address inequities exacerbated by AI technologies.

He advocated for "bias management," suggesting that while subjectivity in archival description is unavoidable, it must be anchored in consistent values that prioritize human rights and historical understanding. Thomas also called for regulatory frameworks to provide clarity and consistency in ethical approaches to AI in archives.

Helen Wong Smith's Insights

Helen echoed the importance of addressing biases in AI-generated metadata and content. She raised concerns about AI's potential to perpetuate inaccuracies and misconceptions, particularly in generative AI that produces new content based on existing data.

She emphasized the necessity of codified record-keeping practices for creators using AI, referencing Jessica Bushey's work on AI-generated images as an emergent record format. Helen reiterated the importance of paradata in documenting not just the tools used but also the reasons, methods, and contexts in which they are applied.

AI and Born-Digital Archives

Enhancing Accessibility and Usability

Helen outlined several ways AI can improve access to born-digital collections:

• Automating metadata creation
• Content classification
• Natural language processing
• Image recognition
• Enhanced search capabilities

However, she also identified barriers to implementation, including:

• Lack of knowledge and competencies within the archival profession
• Reliable technologies and interoperability issues
• Economic constraints and personnel expertise
• Data privacy and security concerns
• Ethical considerations and cultural sensitivity

Addressing Backlogs with AI

Thomas discussed the potential of AI in addressing access issues for backlogged materials, particularly those in less commonly known languages. He highlighted the challenge of insufficient resources and the difficulty in hiring personnel with the necessary language skills to catalog these materials.

Thomas proposed leveraging AI advancements in world languages, possibly in collaboration with companies like Meta, to process and make these materials discoverable. He emphasized that minimal digitization combined with AI could help fulfill the access and preservation mission of archives.

Staying Informed and Managing Overwhelm

Thomas Padilla's Approach

Thomas acknowledged the feeling of overwhelm many professionals experience due to the rapid developments in AI. He recommended:

• Adopting a utilitarian approach to AI as a tool
• Grounding oneself in the history and values of the profession
• Practicing careful curation of information sources
• Utilizing platforms like LinkedIn for professional updates
• Setting up curated Google Alerts for topics like AI in libraries, archives, and regulation

Helen Wong Smith's Resources

Helen suggested leveraging collaborative initiatives like the InterPARES Trust AI project, an international and interdisciplinary effort aimed at designing and developing AI to support trustworthy public records. The project's goals include:

• Identifying AI technologies to address critical records and archival challenges
• Determining the benefits and risks of using AI on records and archives
• Ensuring archival concepts and principles inform the development of responsible AI
• Validating outcomes through case studies and demonstrations

Helen emphasized the importance of engaging with such resources to stay informed and contribute to the ongoing dialogue around AI in archives.

Conclusion

The opening keynote provided valuable insights into the intersection of AI and archives, highlighting both opportunities and challenges. Ray Pun thanked the speakers and attendees, encouraging continued dialogue and exploration of these critical topics.

As AI technologies continue to evolve, the archival profession must navigate ethical considerations, enhance competencies, and develop strategies to leverage AI responsibly. By fostering collaboration, staying informed, and grounding practices in core values, archivists can effectively integrate AI to enhance accessibility and preservation.

Note: This summary is based on the opening keynote delivered by Ray Pun, Helen Wong Smith, and Thomas Padilla at the AI and Libraries 2 Mini Conference.

The Impact of AI on Information Literacy: Introducing the "Artificial Intelligence and Information Literacy" Course

Planning a Credit-Bearing Course on AI and Information Literacy

Presented by Alyssa Russo and David Hurley from the University of New Mexico

Introduction

Alyssa Russo, Learning Services Librarian, and David Hurley, Discovery and Web Librarian at the University of New Mexico (UNM), shared their experiences and plans for developing a credit-bearing course titled "Artificial Intelligence and Information Literacy." This presentation delved into the rationale, structure, and pedagogical approaches they considered while designing the course, aiming to integrate generative AI tools like ChatGPT into information literacy instruction.

Background and Context

The advent of ChatGPT and similar generative AI technologies prompted librarians at UNM to reconsider their approaches to information literacy instruction. Recognizing the profound impact of AI on information systems and user behavior, Russo and Hurley sought to develop a course that not only addressed the practical use of AI tools but also engaged students in critical thinking about the social and ethical implications of these technologies.

At UNM, the library operates within a unique structure, being part of the Organizational Information and Learning Sciences (OILS) program. This affiliation allows librarians to teach credit-bearing courses that explore theoretical aspects of information literacy beyond traditional library instruction. Leveraging this opportunity, Russo and Hurley aimed to create a three-credit course that would encourage students to think critically about how AI reshapes information landscapes.

Inspirational Framework

The presenters drew inspiration from Barbara Fister's perspective on information literacy, emphasizing the need to understand the architectures, infrastructures, and belief systems that shape our information environment. They recognized that generative AI challenges conventional notions of authority, value, and the processes underlying information creation and dissemination.

Hurley noted parallels between current responses to AI and past reactions to disruptive technologies like Google and Wikipedia. In the early days of the web, librarians grappled with similar concerns about information quality and authority. By examining historical responses—ranging from rejection to revolutionary integration—they identified strategies to effectively incorporate AI into information literacy education.

Course Structure and Objectives

Utilizing the ACRL Framework

To provide a solid foundation, the course was structured around the Association of College and Research Libraries (ACRL) Framework for Information Literacy for Higher Education. Each of the six frames served as a module, allowing for a comprehensive exploration of core concepts. This approach also aligned well with the eight-week accelerated format of the course, providing sufficient time for introduction, in-depth exploration, and reflection.

Hybrid Learning Model

Recognizing the benefits of both in-person and online learning, the course was designed as a hybrid. Meeting twice a week, the first session would introduce key concepts and AI tools, while the second would be student-led, fostering a community of practice. This structure aimed to balance guided instruction with collaborative learning, encouraging students to share insights and take ownership of their learning process.

Target Audience and Enrollment

The course was intended for upper-division undergraduates who had prior college-level coursework. This prerequisite ensured that students possessed foundational academic skills, enabling them to engage deeply with complex topics and contribute meaningfully to discussions and projects.

Assignments and Activities

Researchers' Notebook

A central component of the course was the "Researchers' Notebook," an iterative assignment where students documented their evolving thoughts, questions, and interactions with AI tools. This notebook aimed to make the research process visible, emphasizing the development of inquiry skills and reflective practice. By capturing moments of discovery, frustration, and dialogue with AI, students could illustrate their understanding of information literacy concepts in a tangible way.

Module Deep Dive: Research as Inquiry

Focusing on the ACRL frame "Research as Inquiry," one module exemplified the course's pedagogical approach. The objectives were to have students view research as an open-ended exploration and to formulate increasingly sophisticated questions. Activities included:

• Question Formulation Technique: Students engaged in generating, refining, and prioritizing questions related to AI. This collaborative exercise encouraged curiosity and critical thinking, serving as a model for ongoing inquiry throughout the course.
• Walk and Talk Activity: Adapted from the University of Arizona's Atlas of Creative Tools, this exercise involved students pairing up and discussing prompts while walking around campus. Questions like "What is curiosity to you?" and "What challenges does AI face in understanding human questions?" facilitated deeper engagement and embodied learning.

Other Modules and Activities

While the presentation focused on one module in detail, Russo and Hurley outlined plans for other modules based on the remaining ACRL frames. These included activities such as:

• Authority Is Constructed and Contextual: Exploring how authority is established in different information sources and how AI-generated content challenges traditional notions of authority.
• Searching as Strategic Exploration: Comparing search strategies in traditional databases versus AI tools, emphasizing iteration and strategy refinement.
• Information Has Value: Discussing the ethical, legal, and economic implications of AI-generated content, including issues of intellectual property and environmental impact.

Challenges and Reflections

Despite their thorough planning, Russo and Hurley faced challenges in promoting and enrolling students in the course. Both were on different types of leave during critical promotion periods, resulting in insufficient enrollment for the course to run as scheduled. Initially disappointed, they reconsidered and recognized that the course content remained relevant and valuable, even as the initial hype around AI began to settle.

They emphasized that the rapidly evolving nature of AI and its integration into various aspects of society make such a course timely and essential. By sharing their experience, they hoped to inspire others to develop similar courses or integrate these ideas into existing curricula.

Conclusion and Takeaways

Russo and Hurley's presentation highlighted the importance of adapting information literacy instruction to address the challenges and opportunities presented by generative AI. By framing the course around collaborative exploration and critical engagement, they aimed to empower students to navigate and contribute to the evolving information landscape.

Key takeaways from their experience include:

• The value of integrating established frameworks (like the ACRL frames) with new technologies to provide structure and depth.
• The effectiveness of hybrid learning models in fostering community and active participation.
• The importance of reflective and process-oriented assignments, such as the Researchers' Notebook, in making the research process transparent and meaningful.
• The need for flexibility and adaptability in course planning, acknowledging that challenges like enrollment and shifting student interests may arise.
• The relevance of addressing ethical considerations, including environmental impacts and biases inherent in AI technologies.

Final Thoughts

While their course did not run as initially planned, Russo and Hurley remain optimistic about its potential and relevance. They encouraged other educators and librarians to consider similar approaches, emphasizing that the need for critical engagement with AI and information literacy is ongoing.

Their work serves as a valuable model for integrating emerging technologies into educational practices, fostering not only skill development but also critical awareness and ethical considerations among students.

Note: This summary is based on a presentation by Alyssa Russo and David Hurley on planning a credit-bearing course on AI and information literacy at the University of New Mexico.

The Ethics of AI: Navigating the Three Cs of Generative AI

Closing Keynote: The Three Cs of Generative AI in Libraries

Presented by Reed Hepler at the AI and the Libraries 2 Mini Conference

In the closing keynote of the "AI and the Libraries 2 Mini Conference: More Applications, Implications, and Possibilities," Reed Hepler, Digital Initiatives Librarian and Archivist at the College of Southern Idaho, shared valuable insights on the use of generative AI in educational and library settings. With experience spanning educational formats, library environments, and business training, Hepler delved into the ethical considerations and best practices surrounding generative AI tools.

Introduction

Hepler began by acknowledging the diverse perspectives educators and administrators hold regarding generative AI. He identified four primary viewpoints observed at his institution:

1. Fear that student use of ChatGPT and similar tools creates new forms of unethical practices.
2. Confidence that students wish to use ChatGPT effectively and constructively.
3. Concern that generative AI undermines established systems and norms of online learning.
4. Belief that ChatGPT can lead to innovative products and workflows enhancing instructional design and assessment.

Recognizing the need to address these concerns, Hepler introduced a framework he developed to guide ethical and effective use of generative AI: the "Three Cs."

The Three Cs of Generative AI

1. Copyright

Key Question: Who owns the rights to AI-generated products, and how are they created?

Hepler discussed the complexities of copyright in the context of generative AI, posing three critical questions:

• What are the rights and responsibilities of the original creators whose works are used by AI?
• What are the rights and responsibilities of users who employ AI tools?
• Is generative AI an owner, a user, both, or neither in terms of copyright?

He clarified that copyright protects the expression of ideas in any medium and grants exclusive rights to the creator or copyright holder. However, devices, processes, ideas, public domain materials, works by government employees, and recipes cannot be copyrighted.

Hepler emphasized that the current copyright law requires human authorship for protection, raising questions about whether AI can be considered an author. He also highlighted the ongoing debates and legal challenges surrounding the fair use doctrine as it pertains to AI training on copyrighted materials.

He cited examples of copyright battles involving AI-generated works, such as "Zarya of the Dawn," and discussed the implications of using copyrighted content in AI prompts. He stressed the importance of respecting intellectual property rights and advised users to avoid inputting copyrighted material into AI tools unless they own the rights.

2. Citation

Key Question: How should AI tools and outputs be cited, and where did the information originate?

Noting the absence of standardized citation formats for AI-generated content, Hepler emphasized that the purpose of citation is to provide information about sources. He recommended including the following elements in any AI citation:

• Tool name and version
• Date and time of usage
• Prompt, query, or conversation title
• Name of the person who queried the AI
• Link to the conversation or output, if possible

He provided an example of how to cite AI-generated content in APA style, suggesting that users include their name to acknowledge their role in the creation process. He stressed that users should engage in the editing and revision of AI outputs to ensure originality and accuracy.

3. Circumspection

Key Question: What hazards—moral, ethical, educational, or otherwise—should users manage when utilizing generative AI tools?

Hepler outlined several ethical issues associated with AI outputs, including:

• Plagiarism
• Biases
• Repetitiveness and arbitrariness
• Incorrect or misleading information
• Lack of connection to external resources

He discussed privacy concerns, highlighting how AI tools can extrapolate personal data from user inputs, even when users attempt to minimize the information they provide. He emphasized that users should never input sensitive or confidential information into AI prompts.

Hepler recommended several practices to mitigate these risks:

• Informing users about data collection and its purposes
• Obtaining explicit consent for data usage
• Limiting data collection to essential information (data minimization)
• Implementing strict access and use controls
• Anonymizing data in prompts

He also discussed the importance of quality control when using AI-generated content, advising users to:

• Use AI tools for their intended purposes
• Engage in best practices for prompting
• Ask the AI for its sources and verify them
• Find external resources to support AI-generated information
• Analyze outputs for ethical issues, accessibility, and accuracy

Privacy and Ethical Considerations

Hepler delved deeper into privacy harms associated with AI, referencing works by legal scholars such as Danielle Keats Citron and Daniel J. Solove. He noted that privacy laws often require proof of harm, which can be difficult when dealing with intangible injuries like anxiety or frustration resulting from data breaches or misuse.

He highlighted that AI tools like ChatGPT have specific terms of use that assign users ownership of the outputs generated from their inputs. However, users are responsible for ensuring that their content does not violate any applicable laws.

Hepler stressed that despite best efforts, AI tools can still extrapolate personal data, underscoring the importance of being cautious with the information provided to these systems.

Conclusion and Recommendations

Concluding his keynote, Hepler provided a list of references and resources for further exploration of the topics discussed. He reiterated the need for libraries and educators to navigate the evolving landscape of generative AI thoughtfully, balancing innovation with ethical considerations.

He encouraged attendees to remain informed about developments in AI and copyright law, to respect intellectual property rights, and to engage in responsible use of AI tools. By adhering to the "Three Cs" framework—Copyright, Citation, and Circumspection—users can harness the benefits of generative AI while mitigating potential risks.

Final Thoughts

Hepler's presentation offered a comprehensive overview of the challenges and responsibilities associated with generative AI in libraries and education. His insights serve as a valuable guide for professionals seeking to integrate AI tools into their work ethically and effectively.

Note: This summary is based on the closing keynote delivered by Reed Hepler at the AI and the Libraries 2 Mini Conference.

The Real-World Harms of AI in Healthcare: A Closer Look

Ethical Considerations for Generative AI Now and in the Future

Presented by Dr. Kellie Owens, Assistant Professor in the Division of Medical Ethics at NYU Grossman School of Medicine

Dr. Kellie Owens delivered an insightful presentation on the ethical considerations surrounding generative AI, particularly relevant to medical librarians and professionals involved in data services. As a medical sociologist and empirical bioethicist, Dr. Owens focuses on the social and ethical implications of health information technologies, including the infrastructure required to support artificial intelligence (AI) and machine learning in healthcare.

Introduction

Dr. Owens began by situating herself within the broader discourse on AI ethics, acknowledging the prevalent narratives of both awe and panic that often dominate news coverage. She highlighted a split within the field between AI safety—which focuses on existential risks and future catastrophic events—and AI ethics, which concentrates on addressing current, tangible ethical concerns associated with AI technologies.

Referencing the "Pause Letter" signed by prominent figures like Yoshua Bengio and Elon Musk, which called for a six-month halt on training AI systems more powerful than GPT-4, Dr. Owens expressed skepticism about such approaches. She argued that while managing existential risks is important, it is crucial to focus on the real and already manifesting ethical issues that AI poses today.

Real-World Harms of AI in Healthcare

Dr. Owens provided examples of harms caused by AI tools in healthcare, emphasizing that these issues are not hypothetical but are currently affecting patients and providers. She cited instances where algorithms reduced the number of Black patients eligible for high-risk care management programs by more than half and highlighted biases in medical uses of large language models like GPT, which can offer different medical advice based on a patient's race, insurance status, or other demographic factors.

Framework for Ethical Considerations

Building her talk around the five key themes from the Biden administration's Office of Science and Technology Policy's "Blueprint for an AI Bill of Rights," Dr. Owens discussed:

1. Safe and Effective Systems
2. Algorithmic Discrimination Protections
3. Data Privacy and Security
4. Notice and Explanation
5. Human Alternatives, Consideration, and Fallback

1. Safe and Effective Systems

Emphasizing the principle of "First, do no harm," Dr. Owens discussed the ethical imperative to ensure that AI tools are both safe and effective. She addressed the issue of AI hallucinations, where large language models generate false or misleading information that appears credible. In healthcare, such errors can have significant consequences.

She also touched on the problem of dataset shift, where AI models decline in performance over time due to changes in technology, populations, or behaviors. Dr. Owens highlighted the need for continuous monitoring and updating of AI systems to maintain their reliability and accuracy.

2. Algorithmic Discrimination Protections

Dr. Owens delved into the ethical concerns related to algorithmic bias and discrimination. She cited studies like "Gender Shades," which revealed that facial recognition technologies performed poorly on women, particularly women with darker skin tones. In the context of generative AI, she discussed how image generation tools can perpetuate stereotypes, such as depicting authoritative roles predominantly as men.

She highlighted instances where AI models like GPT-4 produced clinical vignettes that stereotyped demographic presentations, calling for comprehensive and transparent bias assessments in AI tools used in healthcare.

3. Data Privacy and Security

Addressing data privacy concerns, Dr. Owens discussed vulnerabilities like prompt injection attacks, where attackers manipulate AI models to reveal sensitive training data, including personal information. She emphasized the importance of protecting users from abusive data practices and ensuring that individuals have agency over how their data is used.

She also raised concerns about plagiarism and intellectual property violations, noting that generative AI models can reproduce copyrighted material without attribution, leading to potential legal and ethical issues.

4. Notice and Explanation

Dr. Owens stressed the importance of transparency and autonomy, arguing that users should be informed when they are interacting with AI systems and understand how these systems might affect them. She cited the example of a mental health tech company that used AI-generated responses without informing users, highlighting the ethical implications of such practices.

5. Human Alternatives, Consideration, and Fallback

Finally, Dr. Owens emphasized the necessity of providing human alternatives and the ability for users to opt out of AI systems. She underscored that while AI can offer efficiency, organizations must be prepared to address failures and invest resources to support those affected by them.

Key Takeaways

Dr. Owens concluded with several key insights:

• Technology is Not Neutral: AI systems are socio-technical constructs influenced by human decisions, goals, and biases. Recognizing this is essential in addressing ethical considerations.
• Benefits and Costs: It is crucial to weigh both the advantages and potential harms of AI applications, including issues like misinformation, environmental impact, and the perpetuation of biases.
• What's Missing Matters: Considering the gaps in AI training data and the politics of what's excluded can provide valuable ethical insights.
• Power Dynamics: Evaluating how AI shifts power structures is important. AI applications should aim to empower marginalized communities rather than exacerbate existing inequalities.

Conclusion

Dr. Owens encouraged ongoing dialogue and critical examination of generative AI's ethical implications. She highlighted the role of professionals like medical librarians in shaping how AI is integrated into systems, emphasizing the need for intentional design, transparency, and a focus on equitable outcomes.

For those interested in further exploration, she recommended reviewing the "Blueprint for an AI Bill of Rights" and engaging with interdisciplinary approaches to AI ethics.

Note: This summary is based on a presentation by Dr. Kellie Owens on the ethical considerations of generative AI, particularly in the context of healthcare and data services.

Navigating the Intersection of AI and Copyright Law in Australia

AI and Copyright Law in Australia: Exploring Options and Challenges

Presentation by an expert on the intersection of AI and Australian copyright law.

Introduction

The speaker delves into the complexities of how Australian copyright law intersects with artificial intelligence (AI), particularly generative AI. The focus is on exploring practical options for Australia to balance AI innovation with the protection of human creators in the creative industries.

Key Premises

1. Australian Copyright Law is Unique: Australia's legal framework differs significantly from other jurisdictions, impacting how AI and copyright issues are addressed.
2. Room for Debate: There's flexibility in how international copyright principles apply to AI, allowing Australia to make deliberate choices about its legal stance.
3. Desirable End State: The goal is to achieve both AI innovation and deployment in Australia, alongside thriving human creators and creative industries.
4. Practical Realities Matter: Any legal approach must consider Australia's position in the global landscape and the types of AI activities likely to occur within the country.

Generative AI in Australia

The speaker emphasizes that generative AI isn't limited to global platforms like ChatGPT or Midjourney but also includes local applications such as government chatbots and educational tools. These smaller models, often built on larger ones, are integral to various sectors in Australia, including government services and businesses.

Five Options for Addressing AI and Copyright

1. Strict Copyright Rules (Status Quo):
   • Maintains the current strong interpretation of copyright law.
   • Results in widespread potential infringement by businesses and government entities using AI.
   • Does not lead to compensation for creators due to training occurring overseas or behind closed doors.
   • Considered a "lose-lose" scenario with a chilling effect on AI development and deployment in Australia.
2. Classic Common Law Compromise:
   • Attempts to balance interests through complex rules and conditional exceptions.
   • Could lead to a prolonged and complicated legal process with little practical benefit.
   • Risks stalling AI innovation due to legal uncertainties.
3. Equitable Remuneration for Creators:
   • Proposes a remunerated copyright limitation for human creators whose works are used in AI training.
   • Involves collective management organizations and statutory licensing.
   • Faces challenges in valuation, distribution, and practical implementation.
4. Lump Sum Levy on AI Systems:
   • Suggests imposing a levy on AI systems capable of producing literary and artistic works.
   • Aims to compensate creators for potential substitution effects (displacement of human labor).
   • Not strictly a copyright issue but more akin to models like the News Media Bargaining Code.
5. Focus on Economic Loss and Market Effects:
   • Allows AI training on copyrighted data but permits rights holders to claim compensation if they can demonstrate economic loss.
   • Acknowledges the difficulty in proving loss and valuing it appropriately.
   • Highlights the complexity of linking copyright infringement to market harm in the AI context.

Challenges and Considerations

The speaker notes that many proposed solutions have significant drawbacks, particularly in terms of practicality and potential negative impact on AI innovation in Australia. Attempts to create a balanced compromise may result in prolonged legal battles and complex regulations that fail to satisfy any stakeholders fully.

Recommended Path Forward

The speaker suggests a pragmatic approach:

• Address Mundane but Impactful Issues: Focus on areas where immediate improvements can be made, such as text and data mining exceptions, especially for sectors outside the core creative industries.
• Reform Liability at the Deployment Stage: Modify laws to ensure that Australian firms using AI, particularly those adopting reasonable copyright safety measures, are not unduly liable for potential infringements.
• Consider Non-Copyright Solutions for Creator Compensation: Explore mechanisms outside of copyright law, such as levies or funds, to address the displacement effects on human creators.
• Implement Technical Copying Exceptions: Introduce exceptions that allow for necessary technical copying during AI training and deployment without infringing copyright.

Conclusion

The speaker concludes that while the intersection of AI and copyright law presents complex challenges, a practical and focused approach can help Australia navigate these issues effectively. By addressing specific areas where legal adjustments can facilitate AI innovation while minimizing harm to creators, Australia can work towards a more balanced and forward-looking legal framework.

Questions and Discussion

The presentation ends with an invitation for questions and further discussion on the topic, emphasizing the need for ongoing dialogue to refine and implement effective solutions.

Note: This summary is based on a presentation discussing the challenges and options for addressing AI and copyright law in Australia.

The Rise of AI and Its Impact on Organizational Trends

Leadership Trends and the Impact of AI: A Conversation with DBS and NeuroLeadership Institute

Featuring Dr. David Rock and Joan, Chief Learning Officer at DBS Group

In a recent session hosted by the NeuroLeadership Institute, Dr. David Rock and Joan, Chief Learning Officer at DBS Group, discussed current trends in organizations, the role of AI, and the importance of understanding human behavior in leadership.

Opening Remarks and Acknowledgments

The session began with an acknowledgment of the traditional custodians of the land, the Gadigal people of the Eora nation in Sydney, Australia. Participants from around the world joined the conversation, highlighting the global interest in leadership and organizational trends.

Introduction to the NeuroLeadership Institute

The NeuroLeadership Institute, led by Dr. David Rock, focuses on making organizations more human and high-performing through science. With operations worldwide, the institute advises a significant percentage of major companies, including 27% of the ASX 200 and 75% of the Fortune 100.

Celebrating DBS Group's Milestone

Joan shared exciting news that DBS Group has exceeded $100 billion in market capitalization. She expressed enthusiasm about discussing leadership and organizational trends with Dr. Rock, noting their decade-long partnership.

Current Organizational Trends and the Role of AI

When asked about trends in organizations today, Dr. Rock highlighted several key points:

• Importance of Understanding Human Behavior: With the rise of artificial intelligence, understanding how humans function is becoming increasingly critical.
• Relevance of Neuroscience Research: The NeuroLeadership Institute's 26 years of research is more pertinent than ever, especially in navigating the AI revolution.
• AI and Leadership: Dr. Rock emphasized that as AI advances, the need to comprehend human leadership and behavior intensifies.

Looking Ahead

The conversation hinted at deeper discussions on leadership, learning innovation, and the challenges and opportunities presented by AI in organizational contexts.

Note: This summary is based on a session hosted by the NeuroLeadership Institute featuring Dr. David Rock and Joan, Chief Learning Officer at DBS Group.