About
I’m an Associate Professor at TU Delft, working in human-algorithm interaction - exploring the messy terrain between people, data and things through a combination of making and thinking. The core of my work is relational prototyping: finding ways to explore and make vivid the relationships formed through and around technology in order to build better futures.
Current research questions include: How can we understand the algorithmically mediated society that we are heading towards? How can we ensure that there is space for people within computational systems, preserving privacy, choice, identity and humanity while making use of the possibilities of emerging technology? How can we work with things that have an increasing sense of agency, from sensing to responding to shaping the world around them? See info on academic work, with more detail on research, teaching and PhD supervision.
In my creative practice I engage with interactions between people and technology. I make music using computers and controllers, and our laptop trio Raw Green Rust has played in many interesting places. I create artworks that explore technological and social phenomena that have been shown internationally (ZKM, Creativity and Cognition, Scottish National Gallery of Modern Art, Talbot Rice Gallery) and have won multiple awards (Lumen Prize, New Technology Art Award).
Upcoming public events
Selected recent papers
- Prompting Realities: Exploring the Potentials of Prompting for Tangible Artifacts Mehrvarz, Mahan and Murray-Rust, Dave and Verma, Himanshu (2025) Proceedings of the 16th Biannual Conference of the Italian SIGCHI Chapter
Prompting Realities: Exploring the Potentials of Prompting for Tangible Artifacts
Designing meaningful tangible and embodied interactions remains challenging due to their situated nature, complex user needs, and the limited programming skills of many users as well as designers. We developed an interaction model where users and LLMs co-perform tangible actions through prompt engineering beyond deterministic logic of commercial smart systems. In this model AI systems interpret natural language descriptions of environmental context, internalize technical functionalities and spatial cues, and translate these into tangible actions. We encapsulated the interaction model within a LLM-enabled tangible artifact as a HCI provotype and conducted an initial exploratory study around it. Our preliminary findings point to opportunities in refinement and reappropriation of such systems over the use period as well as challenges in adapting deictic spatial references.
- (Un)Making AI Magic: A Design Taxonomy Lupetti, Maria Luce and Murray-Rust, Dave (2024) Proceedings of the CHI Conference on Human Factors in Computing Systems
(Un)Making AI Magic: A Design Taxonomy
This paper examines the role that enchantment plays in the design of AI things by constructing a taxonomy of design approaches that increase or decrease the perception of magic and enchantment. We start from the design discourse surrounding recent developments in AI technologies, highlighting specific interaction qualities such as algorithmic uncertainties and errors and articulating relations to the rhetoric of magic and supernatural thinking. Through analyzing and reflecting upon 52 students’ design projects from two editions of a Masters course in design and AI, we identify seven design principles and unpack the effects of each in terms of enchantment and disenchantment. We conclude by articulating ways in which this taxonomy can be approached and appropriated by design/HCI practitioners, especially to support exploration and reflexivity.
- Prototyping with Uncertainties: Data, Algorithms, and Research through Design Giaccardi, Elisa and Murray-Rust, Dave and Redström, Johan and Caramiaux, Baptiste (2024) ACM Trans. Comput.-Hum. Interact.
Prototyping with Uncertainties: Data, Algorithms, and Research through Design
Seen both as a resource and an obstacle to clarity, uncertainty is a concept that permeates many areas of design. As the concept gains prominence in Human-Computer Interaction (HCI), this special issue specifically explores the interplay between uncertainty and prototyping in Research through Design (RtD). We first outline three histories of uncertainty in design, in relation to its philosophical significance, its role in statistical and algorithmic processes, and its importance in prototyping. The convergence of these aspects is crucial as design evolves toward more agentive and entangled systems, introducing challenges such as Design as a Probabilistic Outcome. We then investigate the design spaces for engaging with “being uncertain” that emerge from the papers: from nuancing the relationship between designers and quantitative data to blurring the line between humans, fungi, and algorithms. Finally, we illuminate some preliminary threads for how RtD can navigate and engage with these shifting technological and design landscapes thoughtfully.
- Unpacking Human-AI Interactions: From Interaction Primitives to a Design Space Tsiakas, Konstantinos and Murray-Rust, Dave (2024) ACM Transactions on Interactive Intelligent Systems
Unpacking Human-AI Interactions: From Interaction Primitives to a Design Space
This paper aims to develop a semi-formal representation for Human-AI (HAI) interactions, by building a set of interaction primitives which can specify the information exchanges between users and AI systems during their interaction. We show how these primitives can be combined into a set of interaction patterns which can capture common interactions between humans and AI/ML models. The motivation behind this is twofold: firstly, to provide a compact generalisation of existing practices for the design and implementation of Human-AI interactions; and secondly, to support the creation of new interactions by extending the design space of HAI interactions. Taking into consideration frameworks, guidelines and taxonomies related to human-centered design and implementation of AI systems, we define a vocabulary for describing information exchanges based on the model’s characteristics and interactional capabilities. Based on this vocabulary, a message passing model for interactions between humans and models is presented, which we demonstrate can account for existing HAI interaction systems and approaches. Finally, we build this into design patterns which can describe common interactions between users and models, and we discuss how this approach can be used towards a design space for HAI interactions that creates new possibilities for designs as well as keeping track of implementation issues and concerns.
- Spatial Robotic Experiences as a Ground for Future HRI Speculations Murray-Rust, Dave and Lupetti, Maria Luce and Ianniello, Alessandro and Gorbet, Matt and Van Der Helm, Aadjan and Filthaut, Liliane and Chiu, Adrian and Beesley, Philip (2024) Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
Spatial Robotic Experiences as a Ground for Future HRI Speculations
This work illustrates how artistic robotic systems can provide a reservoir of unfamiliarity and a basis for speculation, to open the field toward new ways of thinking about HRI. We reflect on a collaborative project between design students, a media art studio, and design researchers working with the baggage handling department of a strategic European airport. Engaging with the industrial context, we developed ’meta-behaviours’ - abstracted ideas of processes carried out on the worksite and passed these over to the students who translated them into robotic enactions based on hardware and a form language developed by the media art studio. The resulting visit experience challenges the audience to decode the installation in terms of meta-behaviours and their possible relations to industrial HRI. We used this to reflect on the value of conducting artistic and speculative work in HRI and to distil actionable recommendations for future research.
- Experiential AI: Between Arts and Explainable AI Hemment, Drew and Murray-Rust, Dave and Belle, Vaishak and Aylett, Ruth and Vidmar, Matjaz and Broz, Frank (2024) Leonardo
Experiential AI: Between Arts and Explainable AI
Experiential artificial intelligence (AI) is an approach to the design, use, and evaluation of AI in cultural or other real-world settings that foregrounds human experience and context. It combines arts and engineering to support rich and intuitive modes of model interpretation and interaction, making AI tangible and explicit. The ambition is to enable significant cultural works and make AI systems more understandable to nonexperts, thereby strengthening the basis for responsible deployment. This paper discusses limitations and promising directions in explainable AI, contributions the arts offer to enhance and go beyond explainability and methodology to support, deepen, and extend those contributions.
- Grasping AI: Experiential Exercises for Designers Murray-Rust, Dave and Lupetti, Maria Luce and Nicenboim, Iohanna and van der Hoog, Wouter (2023) AI & Society
Grasping AI: Experiential Exercises for Designers
Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into the functioning of physical and digital products, creating unprecedented opportunities for interaction and functionality. However, there is a challenge for designers to ideate within this creative landscape, balancing the possibilities of technology with human interactional concerns. We investigate techniques for exploring and reflecting on the interactional affordances, the unique relational possibilities, and the wider social implications of AI systems. We introduced into an interaction design course (n=113) nine ‘AI exercises’ that draw on more than human design, responsible AI, and speculative enactment to create experiential engagements around AI interaction design. We find that exercises around metaphors and enactments make questions of training and learning, privacy and consent, autonomy and agency more tangible, and thereby help students be more reflective and responsible on how to design with AI and its complex properties in both their design process and outcomes.