The notion of explainable user interfaces (XUI) has been proposed as a way to address the growing complexity and unpredictability of many UIs. A key question is whether this aspirational goal can be achieved with reasonable amounts of development effort. This paper takes React as its focus as it is one the most widely used web development frameworks. It shows through practical demonstration that the existing event mechanisms within React can be repurposed with the addition of a small amount of annotation to implement one of the proposed XUI techniques.

This paper summarises the third edition of the SYNERGY workshop on Designing and Building Hybrid Human-AI Systems, held at AVI 2026 in Venice, Italy. The workshop addresses the critical challenge of designing AI systems that genuinely augment human capabilities through meaningful combination, moving beyond approaches where humans function as mere cogs in the machine. Directly aligned with AVI 2026's theme of "Interactive Creativity: Agencies, Interfaces, and Ethics", the workshop brings together researchers and practitioners to advance both theoretical frameworks and practical implementations that preserve human agency while leveraging AI's computational power.

Abstract Whilst it is commonly reported that healthcare is set to benefit from advances in Artificial Intelligence (AI), there is a consensus that, for clinical AI, a gulf exists between conception and implementation. Here we advocate the increased use of situated design and evaluation to close this gap, showing that in the literature there are comparatively few prospective situated studies. Focusing on the combined human-machine decision-making process - modelling, exchanging and resolving - we highlight the need for advances in exchanging and resolving. We present a novel relational space - contextual dimensions of combination - a means by which researchers, developers and clinicians can begin to frame the issues that must be addressed in order to close the chasm. We introduce a space of eight initial dimensions, namely participating agents, control relations, task overlap, temporal patterning, informational proximity, informational overlap, input influence and output representation coverage. We propose that our awareness of where we are in this space of combination will drive the development of interactions and the designs of AI models themselves. Designs that take account of how user-centered they will need to be for their performance to be translated into societal and individual benefit.

This workshop explores the evolving landscape of Human-AI collaboration, focusing on Advanced Visual Interfaces and Artificial Intelligence to enhance human cognition. We explore synergistic models of collaboration that merge human insights with AI capabilities, addressing ethical dimensions and practical AI applications. Our goal is to foster rich interdisciplinary dialogue and challenge existing paradigms of human-machine interaction. We aim to redefine interaction paradigms and establish new benchmarks for intelligent systems, ensuring AI not only supports but significantly augments human decision-making processes.

This workshop explores the influence of AI systems on human decision-making - algorithmic authority - and the broader concept of technology dominance, which includes both positive and negative impacts of AI reliance. Drawing from diverse fields such as Human-AI Interaction, Sociology, Epistemology, and Cognitive Science, the workshop will discuss theoretical foundations, empirical studies, and design implications of AI’s role in shaping human judgment and behavior. The objectives are to examine in-depth the concepts of algorithmic authority and technology dominance, and identify metrics for their assessment. The workshop aims to foster interdisciplinary collaboration and produce practical design principles that help to counter risks associated to AI technology dominance and thus foster a responsible use of AI systems.