Steps Towards Building Library AI Infrastructures: Research Data Repositories, Scholarly Research Ecosystems and AI Scaffolding

Abstract

Artificial Intelligence possibilities for Deep Learning, machine learning, neural nets and natural language processing present fascinating new AI library service areas. Most of these areas will be integrated into traditional academic library ‘information’ and ‘digital’ literacy programs and university research environments to enable research faculty, students and library staff. Most university faculty, graduate students and library staff working outside of Computer Science disciplines will require help to enable their data and research towards new AI possibilities. This research overviews methodologies and infrastructures for building new AI services within the ‘third interdisciplinary space’ of the academic library. A library is a very suitable space to enable these new ‘algorithmic literacy’ services. This work utilizes the pragmatic steps taken by Texas State University Libraries to set up good foundations. Data-centred steps for setting up digital scholarly research ecosystems are reviewed. Setting needed data-centred groundwork for library AI services enables research, data and media towards wider global online AI possibilities. Library AI external scholarly communications services are discussed as well as educational methodologies involving incremental steps for foundational AI scaffolding. Bootstrapping tools build on present systems and allow for the later enablement of future AI insights. Pathways are clarified from data collection to data cleaning, analytics and data visualization to AI applications. Focused steps needed are forwarded to move library staff, research faculty and graduate students towards these new AI possibilities. Data-centred ecosystems, retooling and building on present library staff expertise are reviewed. Data research repositories, algorithmic and programmatic literacy are recommended for later AI possibilities. Preliminary AI library working groups and R&D prototype methodologies for scaling up future library services and human resource infrastructures are considered. Recommended emergent pathways are prescribed to create library AI infrastructures to better prepare for a currently occurring global AI paradigm shift.