AI in the Public Eye: Investigating Public AI Literacy Through AI Art (2023)

Hemment, D, Currie M, Bennett S, Elwes J, Ridler A, Sinders C, Vidmar M, Hill, R. Warner, H.

ABSTRACT

Recent advances in diffusion models and large language models have underpinned a new generation of powerful and accessible tools, and some of the most publicly visible applications are for artistic endeavour. Such tools, however, provide little scope for deeper understanding of AI systems, while the growing public interest in them can eclipse notice of the vibrant community of artists who have long worked with other forms of AI. We explore the potential for AI Art – particularly work in which AI is both tool and topic – to facilitate public AI literacies and consider how tactics developed before the current generative AI boom have continued relevance today. We look at the strategies of critical AI artists to scaffold public understanding of AI and enhance legibility for non-experts. This paper also investigates how collaborations between artists and AI researchers and designers can illuminate key technical and social issues relevant to the development of AI. The study entailed workshops between three professional artists who work with AI and a cross-disciplinary set of academic participants. This paper reports on these workshops and presents the intentions and strategies expressed by the artists, as well as insights of relevance to the research community on public AI literacies. We find that critical AI art can link underlying technical systems to structural issues of power and facilitate experiential learning that is situated and embodied, valuing interpretation over explanation. The findings also demonstrate the importance of transdisciplinary conversations around art, ethics and the political economy of AI technologies and how these dialogues may feed into AI design processes.

Cite as:

Hemment, D, Currie, M, Bennett, SJ, Elwes, J, Ridler, A, Sinders, C, Vidmar, M, Hill, RL & Warner, H 2023, AI in the public eye: Investigating public AI literacy through AI art. in FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery (ACM), New York, pp. 931-942, The 2023 ACM Conference on Fairness, Accountability, and Transparency, Chicago, Illinois, United States, 12/06/23. https://doi.org/10.1145/3593013.3594052