Abstract
In the fall of 2022, the Carnegie Mellon University (CMU) Libraries began investigating Keenious—an artificial intelligence (AI)-based article recommender tool—for a possible trial implementation to improve pathways to resource discovery and assist researchers in more effectively searching for relevant research. This process led to numerous discussions within the library regarding the unique nature of AI-based tools when compared with traditional library resources, including ethical questions surrounding data privacy, algorithmic transparency, and the impact on the research process. This case study explores these topics and how they were negotiated up to and immediately following CMU’s implementation of Keenious in January, 2023, and highlights the need for more frameworks for evaluating AI-based tools in academic settings.
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