We present AttentionBender, a tool that manipulates cross-attention in Video Diffusion Transformers to help artists probe the internal mechanics of black-box video generation. While generative outputs are increasingly realistic, prompt-only control limits artists’ ability to build intuition for the model’s material process or to work beyond its default tendencies. Using an autobiographical research-through-design approach, we build on Network Bending to design AttentionBender, which applies 2D transforms (rotation, scaling, translation, etc.) to cross-attention maps to modulate generation. We assess AttentionBender by visualizing 4,500+ video generations across prompts, operations, and layer targets. Our results suggest that cross-attention is highly entangled: targeted manipulations often resist clean, localized control, producing distributed distortions and glitch aesthetics in place of linear edits. AttentionBender contributes a tool that functions both as an Explainable AI-style probe of transformer attention mechanisms, and as a creative technique for producing novel aesthetics beyond the model’s learned representational space.

Increasingly, AI video models are marketed as "world simulators," suggesting their ability to model infinite realities. Despite such claims, these models systematically exclude significant aspects of embodied human experience, particularly sexuality. World Simulator is a video installation exploring the poetic friction between these universal claims and the models’ inherent blindness. To do so, the work feeds explicit gay erotica into an AI video-to-video pipeline. Lacking the training data to recognize these images, the system hallucinates surreal alternatives, transforming intimate acts into banal scenes of kitchen appliances, strange architectures, and abstract flesh. By visualizing the limits of synthetic knowledge, the work challenges the hubris of the "world simulator" label, asking how a system, trained primarily on large filtered video datasets, can claim to simulate the world while remaining structurally blind to the body. Beyond this critique, we question the value of simulation itself, asking what forms of sensual representation might offer more expansive, life-affirming possibilities.

The fourth workshop on Explainable AI for the Arts (XAIxArts) continues to bring together and expand a community of researchers and creative practitioners in Human-Computer Interaction (HCI), Interaction Design, AI, eXplainable AI (XAI), and Digital Arts to explore the role of XAI for the Arts. XAI is a key concern of Responsible and Human-Centred AI, emphasising HCI techniques that make opaque AI models more understandable to people. XAIxArts offers a distinctive lens to examine explainability through creative and artistic domains. The previous workshops explored the landscape and the speculative futures of AI in creative processes. To respond to emerging challenges and contribute to creative and societal transformation more broadly, this workshop focuses on the operationalisation of XAI in the Arts. Specifically, we will: i) critically reflect on emerging practices that encourage diversity and inclusivity in XAI; ii) collectively ideate a library of missing projects to encourage future collaborations and speculations; iii) scope the development of a resource hub for open XAIxArts projects to archive tangible XAI interventions and facilitate future community building with the wider discourse on Human-Centred AI.

Me vs. You is a multi-channel video installation that explores the complexities of queer intimacy by co-opting an AI machine vision system. In this work, footage from a wrestling match is transformed through a generative video pipeline into a fluid interaction that oscillates between aggression and tenderness. The initial wrestling footage is fed through an AI depth map network—designed to separate bodies—before being reconstructed using a diffusion video process. Rather than rendering discrete fighters, the system produces unstable, shifting forms that sensually collide and merge, destabilizing a clear reading of the interaction. The work exploits the machine vision system’s inability to delineate entangled bodies, challenging computational frameworks of classification and control; instead, it repurposes AI as a tool for poetic ambiguity. Situating Me vs. You within experimental filmmaking and AI surveillance debates, this paper examines how emerging technologies can disrupt narrow modes of machine perception and proposes more expansive ways of seeing.

This paper provides a framework for how artists can use critical design to subvert deeply ingrained expectations around technology. I begin by defining the technological understanding of being as introduced by Martin Heidegger and break down expectations of technology into efficiency, control, and pleasure. I then ask if the burgeoning HCI practice of critical design could be an effective tool for artists in subverting these expectations. My methodology to study this question is to install a provocative object, in my case a disobedient kissing robot, in a public space and analyze participants’ reactions to it using both video and survey analysis. Results show over 70% of users experienced some degree of surprise, friction, or disappointment with the device due to broken expectations about how they assume technology should work. Our study concludes with a discussion of what value there is in challenging the status quo, how artists can take this work further, and what element humor has in provocative critical design.