CHI

Science is a complex system comprised of many scientists who individually make decisions that, due to the size and nature of the academic system, largely do not affect the system as a whole. However, certain decisions at the meso-level of research communities, such as the Human-Computer Interaction (HCI) community, may result in deep and long-lasting behavioral changes in scientists. In this article, we provide empirical evidence on how a change in editorial policies introduced at the ACM CHI Conference in 2016 destabilized the CHI research community and launched it on an expansive path, denoted by a year-by-year increase in the mean number of references included in CHI articles. If this near-linear trend continues undisrupted, an article at CHI 2030 will include on average almost 130 references. The trend toward more citations reflects a citation culture where quantity is prioritized over quality, contributing to both author and peer reviewer fatigue. Our exploratory analysis highlights the profound impact of meso-level policy adjustments on the evolution of scientific fields and disciplines, urging all stakeholders to carefully consider the broader implications of such changes.

The ACM CHI Conference has a tradition of citing its intellectual heritage. At the same time, we know CHI is highly diverse and evolving. In this highly dynamic context, it is not clear how the CHI community continues to appreciate its milestones (within and outside of CHI). We present an investigation into how the community’s citations to milestones have evolved over 43 years of CHI Proceedings (1981–2024). Forgetting curves plotted for each year suggest that milestones are slowly fading from the CHI community’s collective memory. However, the picture is more nuanced when we trace citations to the top-cited milestones over time. We identify three distinct types of milestones cited at CHI, a typology of milestone contributions, and define the Milestone Coefficient as a metric to assess the impact of milestone papers on a continuous scale. Further, our findings suggest the potential presence of a Matthew effect at CHI. We discuss the broader ramifications for the CHI community and the field of HCI.

HCI and CSCW research that uses social media data to make inferences about individuals and communities has proliferated in the last decade. Previous studies have elaborated on methodological concerns and challenges and examined the assumptions and values underlying knowledge production through quantification and data. We expand this line of research by making visible and explicit the conventions and practices that establish, sustain, and reinforce current discourses in social media research. We conducted a Critical Discourse Analysis on 84 research papers published between 2010 and 2023 in CHI, CSCW, and GROUP that combine social media data and computational methods. Our findings show that plenty of this work legitimizes social media data as a valid source of information by centering its public availability, unobtrusiveness, and volume. Furthermore, to justify computational techniques, these papers prioritize computational expediency over data and method appropriateness. We argue that these embedded strategies may result in a methodological and epistemological distance between researchers and the studied communities, impacting problem framing, data collection, and finding application. With this work, we join the voices that have advocated for increased reflexivity in HCI and CSCW communities to scrutinize knowledge production and the role of researchers as knowledge producers.

… The CHI conference is the premier venue for publishing research in human-computer interaction. As a result, CHI … It is ranked as one of the top computer science conferences, with an h5-…

Explainable AI (XAI) is concerned with how to make AI models more understandable to people. To date these explanations have predominantly been technocentric - mechanistic or productivity oriented. This paper introduces the Explainable AI for the Arts (XAIxArts) manifesto to provoke new ways of thinking about explainability and AI beyond technocentric discourses. Manifestos offer a means to communicate ideas, amplify unheard voices, and foster reflection on practice. To supports the co-creation and revision of the XAIxArts manifesto we combine a World Café style discussion format with a living manifesto to question four core themes: 1) Empowerment, Inclusion, and Fairness; 2) Valuing Artistic Practice; 3) Hacking and Glitches; and 4) Openness. Through our interactive living manifesto experience we invite participants to actively engage in shaping this XIAxArts vision within the CHI community and beyond.

In this section, we feature reports from conferences, symposia, workshops, and similar events, focusing on discussions where the boundaries of HCI and UX are being challenged and where debate is lively and ongoing.

Human-Computer Interaction (HCI) is a rapidly evolving field. It has undergone many changes, and several current challenges deserve more attention from the community. Meta-research – the study of research practices – offers insights into how a field can refine its methodological frameworks, enhance rigor, and address its challenges. We believe CHI deserves a dedicated space for meta-research. This workshop establishes an open space for HCI scholars in the top conference of the field to explore and discuss meta-research in HCI. We are equally focused on the past, present, and future: what we study, how we document it, how we evaluate, and how we distribute our work. Collateral effects such as mounting career pressures to publish always more are interesting, too. Short term results of this workshop include a research roadmap specifically for HCI meta-research. In the long term, we hope to see this workshop be the initial spark to establishing a permanent HCI meta-research community.

This late-breaking work presents a large-scale analysis of explainable AI (XAI) literature to evaluate claims of human explainability. We collaborated with a professional librarian to identify 18,254 papers containing keywords related to explainability and interpretability. Of these, we find that only 253 papers included terms suggesting human involvement in evaluating an XAI technique, and just 128 of those conducted some form of a human study. In other words, fewer than 1% of XAI papers (0.7%) provide empirical evidence of human explainability when compared to the broader body of XAI literature. Our findings underscore a critical gap between claims of human explainability and evidence-based validation, raising concerns about the rigor of XAI research. We call for increased emphasis on human evaluations in XAI studies and provide our literature search methodology to enable both reproducibility and further investigation into this widespread issue.

emerging approaches, new ideas

This workshop applies human centered themes to a new and powerful technology, generative artificial intelligence (AI), and - among other approaches - particularly to Large Language Models (LLMs) and Foundation Models (FMs). Unlike AI systems that produce decisions or descriptions, generative AI systems can produce new and creative content that can include images, texts, music, video, code, and other forms of design. The results are often similar to results produced by humans. However, it is not yet clear how humans make sense of generative AI algorithms or their outcomes. It is also not yet clear how humans can control and more generally, interact with, these powerful capabilities in ethical ways. Finally, it is not clear what kinds of collaboration patterns will emerge when creative humans and creative technologies work together. Following successful workshops in 2022–2024, we convene the interdisciplinary research domain of generative AI and HCI. Participation is open to seasoned scholars and early career researchers. We solicit descriptions of completed projects, works-in-progress, and provocations. Together we will develop theories and practices in this intriguing new domain.

Recent advancements in general-purpose AI have highlighted the urgent need to align AI systems with the goals, ethical principles, and values of individuals and society. Existing alignment research has been primarily approached as an AI-centered, static, and unidirectional process. However, this unidirectional perspective falls short of taking into account the dynamic and evolving interaction between humans and AI, necessitating a shift toward a bidirectional, interconnected mode of human-AI alignment. This SIG aims to outline the emerging areas of bidirectinoal human-AI alignment research, propose a blueprint of future goals and challenges for fundamental alignment research, and establish a shared platform to bring together experts from HCI, AI, social sciences, and more to advance interdisciplinary research and collaboration on human-AI alignment.

Explainable AI (XAI) is more than just “opening” the black box — who opens it matters just as much, if not more, as the ways of opening it. Human-centered XAI (HCXAI) advocates that algorithmic transparency alone is not sufficient for making AI explainable. In our fifth CHI workshop on Human-Centered XAI (HCXAI), we shift our focus to new, emerging frontiers of explainability: (1) participatory approaches toward explainability in civic AI applications; (2) addressing hallucinations in LLMs using explainability benchmarks; (3) connecting HCXAI research with Responsible AI practices, algorithmic auditing, and public policy; and (4) improving representation of XAI issues from the Global South. We have built a strong community of HCXAI researchers through our workshop series whose work has made important conceptual, methodological, and technical impact on the field. In this installment, we will push the frontiers of work in HCXAI with an emphasis on operationalizing perspectives sociotechnically.

Communication in Human-Robot Interaction (HRI) often focuses on linguistic exchanges, with spoken dialogue providing a natural way for people to interact with robots. While direct verbal interaction can reduce barriers compared to other forms of interaction (e.g., text- or touch-based interfaces), it may also exclude users with speech, language, or cognitive differences, and they may not generalize well across cultures and contexts. Non-linguistic forms of communication, including non-verbal voice interactions and extra-linguistic signals (e.g., gesture, gaze, facial expressions, posture), offer complementary pathways that can enable more inclusive, accessible, and universal interactions. This workshop explores how non-linguistic communication can shape effective human-robot communication and collaboration. We aim to bring together researchers from HRI, conversational AI, linguistics, psychology, and accessibility studies to discuss opportunities, challenges, and design practices for integrating such features. The workshop seeks to advance inclusive design principles, bridge disciplines, and highlight future research directions on communication strategies that empower diverse users in their interactions with robots.

The rise in popularity of general-purpose large language models (LLMs) raises questions around bias and fairness in the decision they make. Do these models reflect the biases and stereotypes present in the data they have been pre-trained on? If so, how should we deal with it? In this talk, we first discuss issues of bias in human data using as an example gender bias in Wikipedia where we looked at how well represented genders are across different categories of articles. We then move on to look at issues of bias in Artificial Intelligence (AI) using as an example political bias in LLMs. We show how it is possible to measure the political standing of different LLMs and to control their standing by telling them to impersonate certain profiles. This also shows what are some of the existing stereotypes (e.g., a museum curator is left-wing and a retired army officer is right-wing) embedded into the LLMs during pre-training. Finally, we discuss how to explore and manage bias existing in LLMs, how these models perform when used for sensitive tasks, and how users tend to trust AI agents for low-risk and high-risks tasks.

Research on Cognitive Personal Informatics (CPI) is steadily growing as new wearable cognitive tracking technologies emerge on the consumer market, claiming to measure stress, focus, and other cognitive factors. At the same time, with generative AI offering new ways to analyse, visualize, and interpret cognitive data, we hypothesize that cognitive tracking will soon become as simple as measuring your heart rate during a run. Yet, cognitive data remains inherently more complex, context-dependent, and less well understood than physical activity data. This workshop brings together HCI experts to discuss critical questions, including: How can complex cognitive data be translated into meaningful metrics? How can AI support users’ data sensemaking without over-simplifying cognitive insights? How can we design inclusive CPI technologies that consider inter-personal variance and neurodiversity? We will map challenges and opportunities for CPI, considering recent AI advancements, and outline a research road map for the foreseeable future.

Summary • The HCI community is being affected by large-scale attacks on science and research • A grassroots initiative of HCI scholars from all backgrounds and career stages are coming together with solutions • We call on the broader HCI community to join us

Large language models (LLMs) have been positioned to revolutionize HCI, by reshaping not only the interfaces, design patterns, and sociotechnical systems that we study, but also the research practices we use. To-date, however, there has been little understanding of LLMs’ uptake in HCI. We address this gap via a systematic literature review of 153 CHI papers from 2020-24 that engage with LLMs. We taxonomize: (1) domains where LLMs are applied; (2) roles of LLMs in HCI projects; (3) contribution types; and (4) acknowledged limitations and risks. We find LLM work in 10 diverse domains, primarily via empirical and artifact contributions. Authors use LLMs in five distinct roles, including as research tools or simulated users. Still, authors often raise validity and reproducibility concerns, and overwhelmingly study closed models. We outline opportunities to improve HCI research with and on LLMs, and provide guiding questions for researchers to consider the validity and appropriateness of LLM-related work.

Surveying prior literature to establish a foundation for new knowledge is essential for scholarly progress. However, survey articles are resource-intensive and challenging to create, and can quickly become outdated as new research is published, risking information staleness and inaccuracy. Keeping survey articles current with the latest evidence is therefore desirable, though there is a limited understanding of why, when, and how these surveys should be updated. Toward this end, through a series of in-depth retrospective interviews with 11 researchers, we present an empirical examination of the work practices in authoring and updating survey articles in computing research. We find that while computing researchers acknowledge the value in maintaining an updated survey, continuous updating remains unmanageable and misaligned with academic incentives. Our findings suggest key leverage points within current workflows that present opportunities for enabling technologies to facilitate more efficient and effective updates.

Leveraging large language models in real-world settings often entails a need to utilize domain-specific data and tools in order to follow the complex regulations that need to be followed for acceptable use. Within financial sectors, modern enterprises increasingly rely on Retrieval-Augmented Generation (RAG) systems to address complex information retrieval in financial document workflows. However, existing solutions struggle to account for the inherent heterogeneity of data (e.g., text, tables, diagrams) and evolving complexity in financial filings, leading to compromised accuracy in critical information extraction. We propose the FinSage framework as a solution, utilizing a multi-aspect RAG framework tailored for data retrieval and summarization in multi-modal financial documents. øurmodel introduces three innovative components: (1) a multi-modal pre-processing pipeline that unifies diverse data formats and generates chunk-level metadata summaries, (2) a multi-path sparse-dense retrieval system augmented with query expansion (HyDE) and metadata-aware semantic search, and (3) a domain-specialized re-ranking module fine-tuned via Direct Preference Optimization to prioritize ground-truth-related content. Extensive experiments demonstrate that FinSage achieves an impressive recall of 92.51% on 75 expert-curated questions derived from surpasses the best baseline method on the FinanceBench question answering datasets by 24.06% in accuracy. Moreover, FinSage has been successfully deployed as financial question-answering system in online meetings, where it has already served more than 1,200 people. The implementation is publicly available at https://github.com/simplew4y/finsage.

Micro-action refers to subtle, low-intensity non-verbal behaviors that can provide insights into an individual's underlying emotions and intentions. Due to its brief duration and significant overlap, identifying these micro-actions poses a challenge for current models. In response to these challenges, this paper proposes a novel multi-feature fusion framework, which extracts coarse-grained body features and fine-grained action features separately. Specifically, we present Temporal Contextualization for fine-grained learning, a cross-frame injection mechanism designed to capture essential spatio-temporal information and introduce a 3D-ResNet Adapter for coarse-grained learning, which aggregates temporal data and facilitates parameter-efficient fine-tuning. In consideration of the task dataset distribution's long-tail nature, the implementation of Feature Decoupling is undertaken, adopting a two-stage training strategy. By conducting experiments, the aforementioned hierarchical multi-feature extraction and aggregation approach has been demonstrated to yield substantial enhancement in Micro-Action Recognition. Our method attains an F1-mean score of 77.75% on the MA-52 dataset, ranking 1st in the 2nd Micro-Action Analysis Grand Challenge in Conjunction with ACM MM'25.