Responsible Operations: Data Science, Machine Learning, and AI in Libraries

OCLC Research Position Paper by Thomas Padilla: “Despite greater awareness, significant gaps persist between concept and operationalization in libraries at the level of workflows (managing bias in probabilistic description), policies (community engagement vis-à-vis the development of machine-actionable collections), positions (developing staff who can utilize, develop, critique, and/or promote services influenced by data science, machine learning, and AI), collections (development of “gold standard” training data), and infrastructure (development of systems that make use of these technologies and methods). Shifting from awareness to operationalization will require holistic organizational commitment to responsible operations. The viability of responsible operations depends on organizational incentives and protections that promote constructive dissent…(More)”.