This project examines how current artistic practices with artificial intelligence (AI) are informed by the intersection of transdisciplinary methods and ideas. It aims to develop theory on the historical context behind these emerging modes of cultural production.
Contextualizing Artistic Practice with Artificial Intelligence is funded by the Portuguese Foundation for Science and Technology (FCT) Individual Call to Scientific Employment Stimulus (CEEC). The research project is hosted by the Research Institute in Art, Design and Society (i2ads) at the Faculty of Fine Arts of the University of Porto (FBAUP).
Project Overview
The proposed research examines how historically grounded theory can inform artistic practice with artificial intelligence. New methods for integrating AI in art (Zylinska 2020) raise complex implications concerning the negative impact these tools may have on artists, the environment, and society (Jiang et al. 2024; Crawford 2021; Pasquinelli 2023; Noble 2018). Many of the issues surrounding AI currently are rooted in much longer tendencies that can inform our understanding of artistic practices with emerging technologies (Lee 2024; 2020). There is a need for conceptual and methodological guidance to aid artists in navigating the rapidly shifting technological conditions for cultural production. This project aims to develop theory on the historical context behind artistic practices working with AI with the goal of informing an understanding of its implications. It considers these emerging methods of cultural production through collaborative practical and theoretical analysis to identify, validate, and bring new insights into defining themes shaping how artists work with technology in their practices.
Contributions
The central contribution of the research will be the development of a theoretical framework to address central challenges, methods, and concepts relevant to the use of AI in artistic contexts. A review of literature and practical cases will assess the current state of the art in this topic relative to its historical background (Lee 2024; 2020) and establish the core elements of the theoretical framework. The findings from the literature review will direct the organization of a series of workshops to collaboratively test, fine-tune, and validate the theoretical framework with students, artists, and academics. Targeted interviews will follow up on and seek greater clarity on issues identified in the workshops. A monthly reading group will serve as a hub to gather community and provide a forum to discuss critical issues that intersect with art and AI. These activities will contribute to the dissemination of research insights through oral presentations, conference papers, and journal articles. The project will culminate in the publication of an edited volume, symposium, and research colloquium inviting critical reflection on defining tendencies in current artistic practices with AI.
Artistic Practice with Artificial Intelligence
This investigation addresses a growing need for theories to better understand AI’s impact on artistic practice. It builds from insights into which aspects set recent developments apart from their historical precursors (Lee 2024; Lee 2020) in order todifferentiate AI hype (Odubela 2024) from salient themes that are currently reshaping visual culture. By involving and inviting the collaboration of not only professional artists but also students and academics, the project seeks to reflect the respective interests of these groups as well as provide valuable tools for continued work in this area.
Preliminary List of References
Carvalhais, Miguel. Art and Computation. Rotterdam: V2_ Publishing, 2022.
Ciston, Sarah. “A Critical Field Guide for Working with Machine Learning Datasets.” Knowing Machines, 2023.
Crawford, Kate. Atlas of AI. New Haven, London: Yale University Press, 2021.
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. Cambridge, London: MIT Press, 2016.
Grba, Dejan. “Deep Else: A Critical Framework for AI Art.” Digital 2, no. 1 (2022): 1–32.
Friedrich, Kathrin, and A S Aurora Hoel. “Operational Analysis: A Method for Observing and Analyzing Digital Media Operations.” New Media & Society 25, no. 1 (2023): 50–71.
Jiang, Harry H., Lauren Brown, Jessica Cheng, Mehtab Khan, Abhishek Gupta, Deja Workman, Alex Hanna, Johnathan Flowers, and Timnit Gebru. “AI Art and Its Impact on Artists.” In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 363–74. AIES ’23. New York, NY, USA: Association for Computing Machinery, 2023.
Kapoor, Sayash, Emily M Cantrell, Kenny Peng, Thanh Hien Pham, Christopher A Bail, Odd Erik Gundersen, Jake M Hofman, et al. “REFORMS: Consensus-Based Recommendations for Machine-Learning-Based Science.” Science Advances 10, no. 18 (May 2024).
Fuller, Matthew, and Eyal Weizman. Investigative Aesthetitcs: Conflicts and Commons in the Politics of Truth. London, New York: Verso, 2021.
Lee, Rosemary. Algorithm, Image, Art. New York: Atropos Press, 2024.
———. “Machine Learning and Notions of the Image”. Copenhagen: IT University of Copenhagen, 2020.
Lee, Rosemary, and Miguel Carvalhais. “Rethinking Media Art in a Time of Pervasive Computation.” Vista: Journal of Visual Culture 14 (November 13, 2024).
Manovich, Lev, and Emanuele Arielli. “Separate and Reassemble: Generative AI Through the Lens of Art and Media Histories.” In Artificial Aesthetics: Generative AI, Art and Visual Media. manovich.net, 2024.
McQuillan, Dan. Resisting AI: An Anti-Fascist Approach to Artificial Intelligence. Bristol: Bristol University Press, 2022.
Noble, Safiya. Algorithms of Oppression. New York: NYU Press, 2018.
Odubela, Ayodele. “V1.01 ~ A Re-Introdution to AI Hype.” AI Anti-Hype, November 15, 2024.
Parikka, Jussi. Operational Images: From the Visual to the Invisual. Minneapolis: University of Minnesota Press, 2023.
Pasquinelli, Matteo. The Eye of the Master: A Social History of Artificial Intelligence. London: Verso, 2023.
Salvaggio, Eryk. “Seeing Like a Dataset: Notes on AI Photography.” Interactions 30, no. 3 (January 5, 2023): 34–37.
Stone, Peter and Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller. “Artificial Intelligence and Life in 2030.” Stanford: Stanford University, 2016.
Zeilinger, Martin. Tactical Entanglements. Lüneburg: Meson Press, 2021.
Zylinska, Joanna. AI Art. London: Open Humanities Press, 2020.