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This chapter proposes a comprehensive AI experimentation policy tailored specifically for libraries. It addresses how librarians should conduct controlled AI experiments with both sensitive and non-sensitive data while upholding ethical principles, privacy standards, and regulatory compliance. The chapter introduces the development of a structured outcome-reporting template designed to embed Monitoring & Evaluation (M&E) practices directly into experimentation cycles. Real consulting cases are used to demonstrate how libraries documented experimental outcomes, ensured transparency, and aligned AI initiatives with organizational objectives. The chapter fills a major research gap by offering the first detailed policy framework combining AI experimentation, ethics, and M&E in library business support.

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AI Experimentation Policies for Libraries: Ensuring Ethical Experimentation, M&E, and Structured Reporting in Business Support Services—Insights from Real Cases

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  • 发表日期：

    2026年

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  • Varun Gupta

摘要

This chapter proposes a comprehensive AI experimentation policy tailored specifically for libraries. It addresses how librarians should conduct controlled AI experiments with both sensitive and non-sensitive data while upholding ethical principles, privacy standards, and regulatory compliance. The chapter introduces the development of a structured outcome-reporting template designed to embed Monitoring & Evaluation (M&E) practices directly into experimentation cycles. Real consulting cases are used to demonstrate how libraries documented experimental outcomes, ensured transparency, and aligned AI initiatives with organizational objectives. The chapter fills a major research gap by offering the first detailed policy framework combining AI experimentation, ethics, and M&E in library business support.