Questioning Convention in Machine Learning: Learning Support for GLAM
Questioning Convention in Machine Learning: Learning Support for GLAM
Dr. Ireti Olowe, University of the Arts London, Creative Computing Institute
How can we leverage technology, machine learning, in particular, to address structured inequality and systemized bias within art historical and museological practice? Transforming Collections aims to transform language, interpretation, description, and discussion around historical, contemporary, and future museum collections. This is a collaborative effort between interdisciplinary researchers from UAL Decolonizing Arts Institute, Tate, and numerous additional partners, machine learning and user experience engineers from UAL Creative Computing Institute. We identify opportunities and explore methods for developing interactive machine learning support tools to manage and examine collections data, cardinal to GLAM practice. In this talk, we present a journey, from building tools with an understanding of what machine learning can bring to GLAM, and overcoming its conventions to building support from an understanding of what GLAM can bring to machine learning.
About the speaker. Ireti’s background, training and experience is in creativity support tools (CSTs) for audiovisual expression and performance. She has led industry research projects in games, virtual and urban experience, Web3, Internet of Things (IoT), HealthTech, FinTech, digital twins, digital agriculture, and smart cities, contributing expertise in human-computer interaction (HCI) and user experience (UX). Ireti is currently a research fellow at UAL’s Creative Computing Institute working on the AHRC funded Transforming Collections discovery project for the Towards a National Collection program. Her current research focuses on user interfaces that enable humans, facilitated by machine learning, to interrogate and manage museum-collections-as-data.