Multimodal retrieval-augmented generation (MRAG) enables creators to steer generative outputs using explicit references such as images or audio. While references enhance creative control, they also amplify concerns about intellectual property, privacy, and provenance. This paper frames MRAG as a creativity support design challenge, proposing a three-axis framework: retrieval substrate and governance, cross-modal representation, and conditioning and interaction. Interviews with 10 visual and sonic creators highlight practical requirements for reference use, identifying key tensions between exploration and governance. The findings inform actionable design primitives that empower creators to manage retrieval, similarity, and reference influence effectively, balancing creative exploration with responsibility.

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.

The third 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 the use of HCI techniques to explore how to make complicated and opaque AI models more understandable to people.The previous workshops moved from mapping the landscape of XAI for the Arts to co-developing an XAIxArts manifesto.To continue driving discourse on XAIxArts, the anticipated outcomes of this workshop are: i) fresh insights into the evolving challenges of AI bias, lack of transparency and barriers to inclusivity through discussion of current and emerging XAIxArts practices; ii) co-developed speculative futures which expand XAIxArts discourse beyond post-hoc rationalisations of AI decisions into the imaginative possibilities of AI as an interlocutor in the creative process; iii) plans for a co-developed proposal of an edited book on XAIxArts; and iv) community expansion and engagement in wider discourses on Responsible and Human-Centred AI.

Reflection is fundamental to creative practice. However, the plurality of ways in which people reflect when using AI Generated Content (AIGC) is underexplored. This paper takes AI-based music composition as a case study to explore how artist-researcher composers reflected when integrating AIGC into their music composition process. The AI tools explored range from Markov Chains for music generation to Variational Auto-Encoders for modifying timbre. We used a novel method where our composers would pause and reflect back on screenshots of their composing after every hour, using this documentation to write first-person accounts showcasing their subjective viewpoints on their experience. We triangulate the first-person accounts with interviews and questionnaire measures to contribute descriptions on how the composers reflected. For example, we found that many composers reflect on future directions in which to take their music whilst curating AIGC. Our findings contribute to supporting future explorations on reflection in creative HCI contexts.

There is a pressing need for sign language to English translation capability to supplement the shortage of sign language interpreters and to provide an aid for training. A modular hybrid design is underway to apply various techniques, including neural networks, in the development of a translation system that can facilitate communication between deaf and hearing people as part of an overall system to automatically translate American sign language to spoken English. The key features to be analyzed are hand motion, hand location with respect to the body, and handshape. A neural network is used to recognize and classify alphanumeric handshapes using Fourier descriptor coefficients as an input vector. The algorithm is described and results shown for applying this technique to experimental images.