AI in Museums: Reflections, Perspectives and Applications

Cover
Contents
Foreword
Introduction
	Reflections
	Perspectives
	Applications
	References
Part 1: Reflections
	The Role of Culture in the Intelligence of AI
		Possible Roles of Archives and Museums in Machine Learning Development
		Critique of AI
		Different Logics
		Potentials and Perspectives
		References
	Why AI Cannot Think
		On Changing the Subject: The Turing test and the Causal Impact of Reality
		On Having the World Embodied in View: Dreyfus and the Worldlessness of Artificial Intelligence
		Humans as Bearers of a Form of Life: McDowell on ‘Life’ as a Transcendental Concept
		References
	AI and Art
		Michael Noll’s Early Computer Art: ‘Human or Machine?’
		AI, Art, and Practice
		Conclusion
		References
	The Hidden Costs of AI
		AI as an Instrument
		AI as an Infrastructure
		AI as an Ideology
		Conclusion
		References
	Dead End or Way Out?
		Beyond Tech‐Solutionism in Art History and Museums
		A Sensitive Approach
		Collaborative Development of Digital Tools
		References
	Power, Data and Control
		Narratives and Practice
		AI in (Museum) Practice
		Models of Ethical AI (Museum) Practices
		Conclusion
		References
	Managing AI
		Towards a Working Definition of Artificial Intelligence
		Artificial Intelligence in Museums—Strategic Approaches
		Ethical Frameworks for Museological Practice
			Reflecting on Underlying Normative Assumptions
			Normative Requests—Desirable Functions of AI Systems
			Choice of AI Models—Decisive Criteria
			The Value of Cultural Heritage Data
		Towards a Methodology of Research Practice
			Between Experimental and Strategic Approaches
			Defining Goals and Success Criteria for AI Projects—A Museological Transfer
		Conclusion—Introducing the Museum as Place for Soft Ethics
		References
	Museum‐AI Assemblages
		Museum‐AI Assemblages
		From Museum Assemblages to Museum‐AI Assemblages: The Case of a Museum Chatbot
		Zooming into the Museum‐AI Assemblage
		Conclusion
		References
Part 2: Perspectives
	AI with Museums and Cultural Heritage
		Framing ‘AI’
			Technique
			Terminology
			Infrastructure
		A Tool for Museums and Cultural Heritage
			Archiving, Cataloguing, Managing Information
			Proposing More Engaging Services for the Audience
			Managing Visitor Experience
			Creating
		Cultural Heritage Practice and AI
			Data Curation
			Cultural Politics and Narratives
		Conclusions
		References
	Troubleshoot?
		State of Research: AI in the Museum Today
		Mapping
			Africa
			Asia
			The Americas
			Australia
			Europe
		Summary
		References
	Digital Curation and AI
		Opportunities
		QURATOR: Automated Curation Technologies for Digitized Cultural Heritage
		Mensch.Maschine.Kultur: AI for Digital Cultural Heritage
		Risks
		Cultural Heritage Institutions and Digital Curation for AI
		Conclusion
		References
	Teaching Provenance to AI
		The Nature of Provenance Texts
		A Provenance‐Specific Annotation Scheme
		Conclusion
		References
	The Funding Program LINK—AI and Culture
		What Are the Application Areas for AI in Culture?
		How Does AI Change Culture and How Does Culture Change AI?
		A Question of Dominance—Art with AI or AI with Art?
		Who is the Creator of Art? Human, Machine, or Coder?
		What Happens When Science and Culture Work Together?
		Culture Shapes the Future
		References
	Discovering Culture with AI
	Post‐Truth
		Archive and Truth
		Becoming a Forger
		Artistic Processing
			Living Organism
			Selective Memories
			The Post‐Truth Archive
		Detecting Fake News
		References
	Impostor Syndrome
		Large Language Models as Entertainment
		Large Language Models as Impostors
		Large Language Models as Storytellers—Used Best in which Contexts?
		References
Part 3: Applications
	Algorithmic Exhibition‐Making
		Case Study
		Collection Data
		Network Construction
		Selection Process
		Exhibition Display
		Discussion
		References
	Evaluating the Blackbox
		Research Process
		Evaluation Steps
		Conclusion
		References
	Clouds of Symbols
		Database
		Symbol Detection
		Results
		References
	xCurator
		Vision und Goals
		Data Foundations
		Participants and Stakeholders
			Audience Segmentation
			Focus Groups
		Experimental Space and the Datalab
		Limits and Obstacles to the Development
		xCurator Tool
		References
	Say the Image, Don’t Make It
		Interacting with the Installation—Co‐Creativity and Accessibility
		Concept
			Wishing Well—A Readymade?
			Say the Image—Don’t Make It
		Technical Details and Functioning
			Software Pipeline
			Dataset
		Ethical Implications
		Conclusion
		References
	CHIM—Chatbot in the Museum
		AI as an Assistant in the Educational Field
		Key Facts about CHIM—Chatbot in the Museum
		Technical Implementation
		CHIM Field Test
		Lessons Learned and Conclusion
		References
	With AI to Art!
		Chatbots in Museum Art Education
		Medusa Memes and More—Project Development in Cooperation with a City District School
		Chat with Six Femmes Fatales—Insights into the Results
		On the Negotiation of Demands—A Conclusion
		Outlook: AI‑Based Dialogues in Digital Art Mediation
		References
	Exploring Beyond the Exhibits
		Related Work
		Methodology
			The Research System
			Workshop
			The Field Study
		Results from Interaction Categories and Codes
			Museum
			Small Talk
			User Statements
			Quantified Knowledge
		Conclusion
		References
	Tracking the Visitor
		Related Work
		Tracking System
		Experimental Setup
		Evaluation
		Discussion
		Conclusion
		References
	Symotiv
		Related Work
		Implementation and Evaluation
			Hardware Setup
			Tracking and Data
			Data Visualization Prototype
		Virtual Reality Reexperience
		Current Developments in Interaction with Visitors
		Conclusion and Future Work
		References
Notes on Contributors
Abstracts
	Mercedes Bunz, The Role of Culture in the Intelligence of AI
	Daniel M. Feige, Why AI Cannot Think
	Arno Schubbach, AI and Art: Arguments for Practice
	Oumaima Hajri, The Hidden Costs of AI: Decolonization from Practice back to Theory
	Lukas Fuchsgruber, Dead End or Way Out? Generating Critical Information about Painting Collections with AI
	Oonagh Murphy, Power, Data and Control: AI in the Museum
	Sonja Thiel, Managing AI: Developing Strategic and Ethical Guidelines for Museums
	Christoph Bareither, Museum‐AI Assemblages: A Conceptual Framework for Ethnographic and Qualitative Research
	Baptiste Caramiaux, AI with Museums and Cultural Heritage
	Isabel Hufschmidt, Troubleshoot? A Global Mapping of AI in Museums
	Clemens Neudecker, Digital Curation and AI: Opportunities and Risks for Cultural Heritage Institutions
	Fabio Mariani, Lynn Rother, Max Koss, Teaching Provenance to AI: An Annotation Scheme for Museum Data
	Tabea Golgath, The Funding Program LINK—AI and Culture: Five Lessons Learned after Five Years
	Luba Elliott, Discovering Culture with AI
	Marion Carre, Post‐Truth: Archives, GPT‑2, and Fake News
	Roland Fischer, Imposter Syndrome: GPT‑3 between Fact and Fiction
	Tillmann Ohm, Algorithmic Exhibition‐Making: Curating with Networks and Word Embeddings
	Nicole High‐Steskal and Rainer Simon, Evaluating the Blackbox: Linking Viennese Art through AI
	Lukas Pilka, Clouds of Symbols: The Digital Curator Project
	Sonja Thiel and Etienne Posthumus, xCurator: AI Curation Tool for Museum Data & User Empowerment
	Yannick Hofmann and Cecilia Preiß, Say the Image, Don’t Make It: Empowering Human‐AI Co‑Creation through the Interactive Installation Wishing Well
	Oliver Gustke, Stefan Schaffer, Aaron Ruß, CHIM—Chatbot in the Museum: Exploring and Explaining Museum Objects with Speech‐Based AI
	Melanie Fahden and Anja Gebauer, With AI to Art! Chatting with Helen of Troy and Co. through IBM Watson
	Ana Müller, Michael Schiffmann, Anke Neumeister, Anja Richert, Exploring Beyond the Exhibits: Creating Knowledge for Social Robots in Public Spaces
	Franz Koeferl, Matthias Zuerl, Jitin Jami, Jindong Li, Dario Zanca, Bjoern Eskofier, Tracking the Visitor: Optical Indoor System for Visitor Research in Museum
	Michael Zöllner, Markus Bosl, Dirk Widmann, Moritz Krause, Symotiv: Virtual Insights into the Symphony Orchestra