We want AI to serve society and we believe that public cultural institutions should play a role in supporting technological innovation to those ends. This research strand investigates public-to-private value transfer, possible governance models, and developmental pathways for creative R&D. 

From artistic practice emerge new approaches to technical and narrative aspects of machine learning/artificial intelligence (AI/ML) continuing a lineage of artistic endeavors building/critiquing computational systems both as tools for communication and analysis. Since 2019 we have been tracking the development of artistic practices that use AI/ML and how they shape the use and culture of these technologies.

Artists use AI/ML to bring new capabilities to their artistic workflow. Through interviews with artists and developers, we are compiling a database of the critical tools deployed in AI/ML practices. With a better view of the infrastructure that underpins these practices, we can advocate for what artists need.

We want artists to be able to work upstream of technological development in order to demonstrate AI/ML’s potential to be a creative and social medium. This requires new kinds of institutional capabilities aimed not only at showcasing but also at developing and supporting the technical process of these complex works.  We take a self-reflexive approach to this research, analysing and prototyping solutions within our own institutions (Serpentine and King’s College London).