AI is now embedded in healthcare, finance, policy, and many other domains, yet genuine human-AI synergy - combined performance that exceeds what either party achieves alone - is uncommon. Meta-analyses show that AI assistance tends to improve human performance compared to working alone, but studies finding true synergy are scarce. We call this persistent shortfall the synergy gap. Most current work treats human-AI combination as an engineering problem and concentrates on interpretability, trust calibration, or interface design. These matter, but they cover only part of what determines whether combination works. Closing the synergy gap, we argue, requires explicit engagement with a wider design space. We map that space through six interconnected elements: sociotechnical context, decision-making frameworks, human decision participants, AI capabilities, interaction, and holistic evaluation. For each element, we describe what it covers, how it shapes the others in practice, and what it implies for design. The result is a shared vocabulary for practitioners building hybrid systems, an analytical lens for researchers studying combination patterns, and a starting point for evaluators interested in the full quality of human-AI decision-making rather than accuracy alone.

Many scientific fields of study use formally established reporting standards to foster research and experimental design, transparency, replicability, peer review, and student training. Examples include CONSORT in medicine, the What Works Clearinghouse in education, JARS in psychology. Such standards yield agreement on study reporting and evaluation, even if using different methodologies. CHI has not adopted reporting standards. Like other fields, CHI has seen an increased number of low-quality submissions and reviews fueled by AI. This panel’s objective is to discuss advantages and barriers of adopting reporting standards for SIGCHI. Panelists include representatives with significant experience creating, adopting and operationalizing reporting standards in adjacent fields: software engineering, CS education, and Programming Languages. The panel will include an overview of the history of reporting standards, a live demo of a standards-based peer review system, discussions of opportunities, challenges, limitations for SIGCHI reporting standards, and an interactive discussion between attendees and panelists.

This paper reports on the theoretical and practical issues raised during the development and deployment of Frasan a mobile web app for An Iodhlann a small island archive. Frasan has been used as a motivating example in previous literature including issues of low-connectivity, deep accessibility, community engagement and projected table top interactions. A particularly unusual feature was the use of a mix of ‘proper’ maps, that is standard projections, and ‘improper’ maps, those designed to emphasise locality with a more artistic and interpretative form. The paper reports on the obvious success measures in terms of engagement with the app and physical footfall at An Iodhlann. However more critical were the changes of archival practice and community engagement with heritage, which have continued beyond the life of the app itself. The paper draws out lessons for future platforms for community heritage and mapping.

A User Experience Research Point of View (UXR PoV) is a perspective based on data, evidence, and insight that shapes how you observe, interpret, and represent the needs of your target users. We need to equip UX Practitioners with the essential tools needed to develop and articulate a persuasive PoV. Our mission is to support professionals in preparing and establishing a compelling narrative that aligns with the needs of their stakeholders. We are developing a UXR playbook that defines a set of plays and instructions for practitioners to build, establish, and land a compelling UX Research POV. The proposed workshop offers an opportunity to hear from HCI Researchers, UX Research professionals and cross-functional partners involved in design processes to extend the foundations already laid and create a more detailed UXR POV playbook.

The popularity of accessibility research has grown recently, improving digital inclusion for people with disabilities. However, researchers, including those who have disabilities, have attempted to include people with disabilities in all aspects of design, and they have identified a myriad of practical accessibility barriers posed by tools and methods leveraged by human-computer interaction (HCI) researchers during prototyping. To build a more inclusive technological landscape, we must question the effectiveness of existing prototyping tools and methods, repurpose/retrofit existing resources, and build new tools and methods to support the participation of both researchers and people with disabilities within the prototyping design process of novel technologies. This full-day workshop at CHI 2025 will provide a platform for HCI researchers, designers, and practitioners to discuss barriers and opportunities for creating accessible prototyping and promote hands-on ideation and fabrication exercises aimed at futuring accessible prototyping.

In this paper, we report on a three-year endeavour that fostered 18 collaborations between academic and non-academic organizations to co-create responses to social (in)justice issues in digital societies. The projects and range of individuals and organisations connected to this programme offer a snapshot of the state of social justice thinking within the UK digital economy research sector. Our analysis shows how the programme’s constellations of actions enacted different modes of resistance attempting to reshape people’s relationship to power dynamics, addressing institutions and exposing systems, and developing and restoring values for social justice. We explore how these efforts invite nuanced understanding of what constitutes resistance in knowledge co-production endeavours and how they helped surface tensions at the intersection of agencies and the distribution of responsibilities. Drawing from our insights and experience, we discuss implications for HCI concerned with the creation of the conditions for social justice in our digital societies.

This paper argues that user interfaces need to be explainable whether or not they contain artificial intelligence components. Even with the best design, complex applications often leave users confused; this is exacerbated on small touchscreens, where small slips can lead to markedly different outcomes and when notifications or intelligent agents may autonomously change the interface. This can be disorienting even for the most tech savvy user, but doubly so for those less confident or with motor-control issues. We are often left asking "what just happened?" or "how can I do this again?". We need explainable user interfaces.

While XAI focuses on providing AI explanations to humans, can the reverse - humans explaining their judgments to AI - foster richer, synergistic human-AI systems? This paper explores various forms of human inputs to AI and examines how human explanations can guide machine learning models toward automated judgments and explanations that align more closely with human concepts.

From applications in automating credit to aiding judges in presiding over cases of recidivism, deep-learning powered AI systems are becoming embedded in high-stakes decision-making processes as either primary decision-makers or supportive assistants to humans in a hybrid decision-making context, with the aim of improving the quality of decisions. However, the criteria currently used to assess a system's ability to improve hybrid decisions is driven by a utilitarian desire to optimise accuracy through a phenomenon known as 'complementary performance'. This desire puts the design of hybrid decision-making at odds with critical subjective concepts that affect the perception and acceptance of decisions, such as fairness. Fairness as a subjective notion often has a competitive relationship with accuracy and as such, driving complementary behaviour with a utilitarian belief risks driving unfairness in decisions. It is our position that shifting epistemological stances taken in the research and design of human-AI environments is necessary to incorporate the relationship between fairness and accuracy into the notion of 'complementary behaviour', in order to observe 'enhanced' hybrid human-AI decisions.

In this paper, we highlight what is missing in the approach to architecting and developing AI models that would mean performance is translated into effective hybrid systems. We conclude people, place and purpose should drive new architectures that support rich interaction through tractable representations that will underpin success. We call for the data-driven ML community to embrace the consideration of tractable representations in the architecture of algorithms and place a responsibility on HCI researchers to unwrap and expose the significant factors in the design space that are critical for successful hybrid decision-making in the real world.