Deep Generative Models (DGM) have had a great impact both on visual art and broader visual culture. In this research-through-design project we investigate the use of a DGM for helping museum visitors explore the aesthetics of Edvard Munch’s art. We designed and built an interactive drawing table that allows a user to explore a StyleGAN model trained on sketches by Edvard Munch. The paper makes two novel contributions: 1. It presents a system that allows users to interact with a DGM by drawing on paper (rather than the typical text prompts used by most current systems). 2. We demonstrate how this mode and quality of interaction establish a unique perspective on Munch’s drawings as a practice. Through qualitative evaluation, we discuss how this setup led users towards a specific hermeneutic drawing strategy that enables building competency with the model and by proxy the data it is trained on. We suggest that the resulting interaction may contribute to an "education of attention" helping museum visitors to become attentive to certain visual qualities in Munch’s drawing practice. Finally, we discuss how the concepts of technological mediation and relationality are useful for designing how the output of a DGM is understood by its users.

Ongoing efforts to turn Machine Learning (ML) into a design material have encountered limited success. This paper examines the burgeoning area of AI art to understand how artists incorporate ML in their creative work. Drawing upon related HCI theories, we investigate how artists create ambiguity by analyzing nine AI artworks that use computer vision and image synthesis. Our analysis shows that, in addition to the established types of ambiguity, artists worked closely with the ML process (dataset curation, model training, and application) and developed various techniques to evoke the ambiguity of processes. Our finding indicates that the current conceptualization of ML as a design material needs to reframe the ML process as design elements, instead of technical details. Finally, this paper offers reflections on commonly held assumptions in HCI about ML uncertainty, dependability, and explainability, and advocates to supplement the artifact-centered design perspective of ML with a process-centered one.

HCI projects bringing digital and interactive technologies into art museums are affected by a conception of the relation between art and design that narrows the available design space. This is often done by positioning such technologies either as presenting information about artworks, as artworks themselves, or by taking a ’hands-off’ approach. Aiming to re-investigate this conceptual space, we draw on an enactive approach to art developed by Alva Noë and, by means of a design example (an installation in the MUNCH museum), discuss how this approach redirected the design process. Specifically, how it affected the way the design team related to the original artworks and their history, and how they approached evaluating their work. We show by example how understanding such technologies in terms of how they educate visitors’ attention, similarly to art critique, allows designers to participate in correspondence with artworks using unique material, aesthetic, and embodied means.

Digital ways of representing fine art are significantly changing the way art is put on display. New exhibition formats including digital projections of fine art allow us to rethink how we can use technology to create art experiences. However, replicating the existing relationships between art, meta-text and visitor might not be the right way. In this project, I explore new ways of creating technologically enabled experiences with fine art paintings based on John Dewey’s concepts of aesthetic experience and expression. Utilizing a constructive design research approach, I create concrete prototypes and designs that expand research on aesthetics of interaction to propose new ways for museums to create art experiences.