Overview

Artificial intelligence is transforming human creativity and the study of art. Yet it is a technology that is difficult to understand from a position outside computer science. This timely volume, Artificial Intelligence and Art History, investigate tensions and opportunities that are arising in human-machine ‘dialogues’ about visual art. Contributors explore recent developments in machine learning and computer vision and debate whether algorithmic analyses of art open new possibilities for human seeing. Do quantitative methodologies threaten humanistic discourses about cultural artefacts? Alternatively, can working at scale offer fresh perspectives on traditional conceptions of, and approaches to, artistic style, methods, and techniques? The chapters in this volume demonstrate how a range of technologies falling under the umbrella of ‘AI’ challenge the epistemological ambitions of both humanistic and scientific study while also addressing the consequences of understanding ‘vision’ as a metaphor for a computational processing. By investigating how AI and computer vision are working – or might work – in partnership with art historical research methods, this volume also interrogates urgent ethical questions that are impacting on research agendas in this interdisciplinary field.

Full Product Details

Author:   Kathryn Brown
Publisher:   Liverpool University Press
Imprint:   The British Academy
Volume:   285
ISBN:  

9781805966609

ISBN 10:   180596660
Pages:   224
Publication Date:   13 March 2026
Audience:   General/trade ,  General
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

List of Figures Notes on Contributors Introduction KATHRYN BROWN How to Explain Pictures to a Dead Hare: Computer Vision and the Art Theory of Joseph Beuys AMANDA WASIELEWSKI A Decade of Bridging Computer Science and Art History EVA CETINIĆ Computer Vision: A ‘Period Eye’ for the 21st Century? KATHRYN BROWN Digital Art History for Datasets: For an Iconology of AI LEONARDO IMPETT Ekphrasis Reloaded: Text-to-Image Models and Generative AI NURIA RODRÍGUEZ-ORTEGA Experiments in the Relationship between Art History and Text-to-Image Models AMALIA FOKA Realism As Style? Leveraging Latent Diffusion Models for Capturing the Style of Realist Images MANAS MEHTA, ZHUOMIN ZHANG, ELIZABETH C. MANSFIELD, JIA LI, JOHN RUSSELL, JAMES Z. WANG Images of Photography: How Computer Vision Frames the Medium TRACY STUBER Conclusion: Art and Intelligence KATHRYN BROWN Index

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Author Information

Kathryn Brown is Reader in Art Histories, Markets and Digital Heritage at Loughborough University. Her books include Women Readers in French Painting 1870–1890 (2012), Matisse’s Poets (2017), ed. Digital Humanities and Art History (2020), Henri Matisse (2021), Dialogues with Degas (2023), and Art Auctions: Spectacle and Value in the 21st Century (2024). She has held visiting fellowships at the Center for Advanced Studies in the Visual Arts (Washington DC), the Humanities Research Centre of the Australian National University, Tulane University, the Beinecke Library (Yale University), and the Getty Foundation. Brown’s research has been funded by the Arts and Humanities Research Council (UK), the British Academy, the Independent Social Research Foundation, and the Terra Foundation for American Art. She is the series editor of Contextualizing Art Markets for Bloomsbury Academic.

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