Artificial Intelligence and the Useful Art Museum: A Cross-Disciplinary Approach Towards Machine Learning and its Implications in the Museum Sphere

Lead Research Organisation: University of Manchester
Department Name: Arts Languages and Cultures

Abstract

This proposed research will explore the contribution of Artificial Intelligence (AI) to the public art museum. Through practice-based, collaborative research with industry partners in the arts and non-arts sectors, this project will develop knowledge of the role of AI in a cultural environment and understand what impact AI will have on public trust. Specifically, this research will investigate the following key questions:
(1) What is the role and potential uses of AI as a curatorial strategy?
(2) How can AI be used to interpret and classify existing collections and inform acquisition?
(3) In what ways will the use of AI in museums challenge and/or enhance public trust?

This research will explore how AI will curate, classify and cluster big data sets, whilst acting as a co-producer of museums, and will discover the implications of new forms of intelligence embedded within museums; asking whether algorithmic outputs are aligned with (new) curatorial strategies, museum stakeholders and cultural policies. This research will question how ML may inform curatorial practice and whether it will introduce bias or as yet unpredictable and currently unknown patterns. This aims to push the art historical discourse beyond common boundaries, gathering knowledge with the help of algorithms and creating new connections between objects, their meanings and their place within museum collections. It is particularly important in the digital humanities 'to contest and transform particular institutional structures' (Bassett et al., 2017), especially as museums have often been seen as institutions where social inequalities have been 'constituted, reproduced and reinforced' (Sandell, 2005). This project will significantly contribute to the field of digital humanities, to critically reflect and understand 'how these technologies operate to structure the world around them, and in doing so transform humanities knowledge and practice' (Berry and Fagerjord, 2017).

Furthermore, this research will explore how AI can help to foster the social mission of useful museums for the public - away from a 'disciplinary museum' (Hooper-Greenhill, 1992) towards a diverse museum that is digitally fit and aware of its social responsibilities - being a transparent (Rader et al., 2018) and useful place where audiences/users can gain familiarity with AI, enabling scholars to research interactions and to provide explanations.

I propose to undertake practice-based, interdisciplinary and applied research which will explore the research questions through investigation of curatorial practices, exhibition design and display which draw on AI and the properties of ML and algorithms, and of audience responses to art which is (co)-produced with, filtered, mediated or classified by AI technologies.

The methodology will involve partnership with museums and with collaborators from the creative industries and other sectors who are working with AI within their research practices, and who are seeking opportunities for public engagement and co-production in which to test these ideas. Partners who have already agreed to join this project are Alistair Hudson, Director of the Manchester City Galleries and the Whitworth, and Prof Richard Taylor, BNFL Chair in Nuclear Energy Systems at the UoM's Dalton Nuclear Institute, which applies AI technologies to support research in nuclear science and is fostering cross-disciplinary research via its BEAM network.
Specifically, this project will discover new ways of implementing immersive and AI technologies in a way that will be useful to four main research stakeholder constituencies:
Museum sector/non-arts and cultural industrial sectors/academic/the public.
BNFL Chair in Nuclear Energy Systems at the University of Manchester's Dalton Nuclear
Institute, which applies AI technologies to support research in nuclear science and is fostering
cross-disciplinary research via its BEAM network.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509565/1 01/10/2016 30/09/2021
2302434 Studentship EP/N509565/1 01/10/2019 30/09/2022 Lukas Nohrer
EP/R513131/1 01/10/2018 30/09/2023
2302434 Studentship EP/R513131/1 01/10/2019 30/09/2022 Lukas Nohrer