AI Experimentation Policy for Libraries: Balancing Innovation and Data Privacy.

This paper presents a strategic framework for librarians to ethically experiment with AI technologies, emphasizing data privacy and compliance. Developed through a consulting project, it categorizes library tasks based on frequency and data sharing needs, resulting in four AI experimentation strategies: Proactive Optimization, Controlled Experimentation, Opportunistic Experimentation, and Conservative Approach. Practical examples, like AI-assisted reference services for entrepreneurs, illustrate responsible experimentation without sensitive data sharing. The framework recommends documenting outcomes for evaluations and integrating Monitoring & Evaluation (M&E) to align AI experiments with library objectives, ensuring AI’s ethical and effective use in enhancing library services. This article suggests that to enhance the practical implementation of the proposed AI experimentation framework and policy in libraries, it is crucial to help librarians become familiar with identifying sensitive data, converting real data into synthetic data, and understanding the implications of sharing information with commercial large language model (LLM) service providers. It highlights the need for foundational training on data privacy, ethical AI practices, and risk assessment, offering solutions such as checklists, training programs, and knowledge-sharing platforms to empower library professionals. [ABSTRACT FROM AUTHOR]