Computational Culture: Transforming Archives Practice and Education for a Post-Covid World

Abstract

This paper takes a landscape view of archives practice now operating in a sea of human digital behavior, interacting with computational systems embedded in real and virtual life, part of our complex global digital ecosystem driving cultural and social change. We envision a new computational archives framework, designed to be user-centric, in ways that integrate traditional archival practice into an overarching computational framework incorporating structured and unstructured data, computational tools, AI (artificial intelligence), ML (machine learning) , robotics, and automation intended to aid in management and public engagement with physical, digitized, and born-digital documents. Set in a networked environment of increasing computing power, this “more than human” system derives from the latest computing advances from NLP (natural language processing) and image recognition to artificial neural networks. We envision an archives system that is at once complex and integrated into a new inclusive and diverse cultural fabric. This paper covers general issues that have been accelerated by the Covid-19 pandemic, together with two institutional case studies.