Algorithmic management (AM) is reshaping work in many industries. However, what is done to redress potential risks is little understood. This study explores how trade unions, employers, and government actors assess AM-related occupational safety and health (OSH) risks and their strategies to understand how industrial relations could influence the safety and health of workers managed by digital technologies. Drawing on the Pressure, Disorganisation and Regulatory failure (PDR) model and interview and document data from Sweden, we find a gradually increasing interest in AM in the early 2020s among the government and the social partners. Unions learn, inform, and bargain about AM; employers enact 'healthy discipline'; and government agencies inspect digital risks in workplaces. Moreover, economic and reward pressures contribute to AM-associated OSH risks. Disorganisation manifests as a lack of knowledge about the OSH effects of AM, leading to ineffective OSH management. Regulatory failure is reflected in new EU regulations stalling national-level initiatives, since the overlapping regulations complicate the enforcement of existing OSH regulations. This study highlights the crucial role of trade unions in advancing the agenda on AM-related OSH risks. It also makes a theoretical contribution by extending the PDR model, offering insights into the driving forces shaping AM and compromising OSH beyond the workplace level - highlighting wider politico-economic and institutional dynamics influencing OSH.

Public sector agencies are rapidly adopting AI systems to make critical decisions that span across services such as benefits eligibility, housing and homelessness services, child welfare services, mobility, inspections, and more. This foregrounds long‑standing HCI concerns about participatory design in settings marked by institutional constraints, public accountability, and uneven power dynamics. While there is broad agreement that community‑centered practices are necessary, practitioners still wrestle with three practical questions: (1) which participatory methods fit distinct stages of the AI lifecycle; (2) what to measure to show that engagement influenced problem framing, data, model, design, or policy; and (3) how procurement and vendor management can make participation feasible and verifiable when agencies acquire AI tools rather than build in-house. This workshop will focus on two themes: methods and measures, to identify practical ways of showing how participation shapes design and policy, and procurement and contracts, to explore how engagement requirements can be embedded in vendor agreements.

As misinformation proliferates online, large language models (LLMs) have been proposed as a promising tool to accelerate fact-checking workflows. While LLMs demonstrate strong performance in tasks such as text annotation, their capabilities in generating fact-checking reports remain uncertain. To investigate how media experts evaluate LLM-generated fact-checking reports, we conducted a 2 (Source: human vs. LLM) X 2 (Disclosure of Source: yes or no) between-subjects online experiment with media professionals (N=274). Our analyses reveal that experts perceive LLM-generated reports as significantly less useful than human-written reports; and such differences become larger when participants are not aware of the source. However, LLM-generated fact-checking reports were rated as accurate and logical as human-authored ones. Party affiliation plays a role in predicting perceived logicalness. Our findings advance the understanding of experts’ evaluation of LLM-generated content within the context of misinformation, which provides important theoretical contributions to HCI and communication theories as well as practical implications for the field.

Platform workers often experience isolation in their work. They use online forums to connect, but their moderation remains underexplored. Article 20 of the EU Platform Work Directive requires digital labour platforms to provide workers with a “communication channel” and leaves interpretation for how to design it up to the platforms. To inform this issue, we qualitatively analyse community rules and moderator comments across 28 worker subreddits. We show how moderators work to reduce harms such as racism and doxxing, cultivate their community through curation, and decide whether to enforce or resist work platform policy. The discussion presents implications for design for worker communication channels. The channels should be spaces with independent moderation and data protection-by-design that enable workers to safely build collective knowledge without fear of platform monitoring. Future work should follow implementations during transposition and test which governance and interface choices produce trust and capacity for collective action. Our contribution is to surface the governance dimension of worker communication and to translate these insights into design implications for future channels.

Debates over open source and openness in artificial intelligence have intensified as policymakers, researchers, and practitioners grapple with how foundation models should be developed and governed to balance innovation, accountability, and public interest. However, there has been limited empirical work examining how diverse stakeholders collectively understand and negotiate responsible openness in AI, particularly through participatory processes that extend beyond industry-led definitions and frameworks. This paper presents findings from a multi-sectoral workshop grounded in futures thinking and participatory design methods. The workshop generated co-created visions of desirable futures and the role of AI, alongside a set of action pathways and a research roadmap focused on responsible open source and openness in AI. This paper makes three key contributions. First, it empirically documents the co-created visions, actions, and research priorities. Second, it identifies four core tensions that emerged as participants translated high-level aspirations into concrete actions, revealing conflicting interpretations of openness regarding its purpose (as an end or a means), its scope (expansion versus meaningful access), and its operation (mandatory versus conditional, sufficient versus dependent on governance and use). These tensions illustrate that responsible openness is not a singular technical solution, but a negotiated sociotechnical project shaped by values, positionalities, and priorities. Third, the paper advances methodological approaches in AI governance by demonstrating how participatory futures methods can surface plural visions, actions, and research priorities that extend beyond dominant, largely corporate, narratives, offering empirical insight into how openness, power, and accountability are negotiated in practice.

To broaden participation in computing and AI design, researchers urge supporting individuals to probe datasets for biases that technologies might amplify. Critically probing data requires first recognizing that it is not neutral, achievable through data contextualization—understanding data contexts including its origin and contents to recognize opportunities or shortcomings. We created a web-tool, “Contextualizing Datasets”, that helps users contextualize data by guiding them in data exploration and different stakeholder interactions. We applied this to an educational case study—using 311 data for allocating government flood resources—with a graduate class. Our findings suggest the tool helped scaffold students in contextualizing data to critically question data curation methods and stakeholder representation, with the local case study context supporting them to draw from lived experiences. From our results, we share ways to improve and re-purpose Contextualizing Datasets and reflect over how it can be leveraged to address generative AI concerns.

As AI deployment accelerates globally, the urgency for responsible AI(RAI) frameworks has catalysed unprecedented policy initiatives and research investments worldwide. The CHI community, positioned uniquely at the intersection of technology, people, society, and values, has a crucial role in shaping RAI development. This meet-up brings together international HCI researchers working across value-sensitive design, human-centered AI, ethics, explainability, trustworthy AI, sustainability and equitability to foster collaborative dialogue on CHI's perspectives and potential in RAI. Through an interactive town hall format featuring community standups, democratic topic selection, and structured roundtable discussions, we will generate actionable outcomes including network building, knowledge sharing, and concrete collaboration opportunities for the CHI community around RAI. This meetup would serve as a foundational response from the CHI community to establish priorities, forge connections, and catalyse the next generation of RAI research that centers human values, promotes fairness, and advances democratic principles in an era of AI transformation.

While AI is often introduced into organizations to drive innovation and efficiency, many adoption efforts fail as workers resist and struggle to integrate these systems. These failures point to a deeper issue: workers, the very people expected to collaborate with AI, are often invisible in decisions about how AI is designed and used. Drawing on interviews with professionals who interact with AI systems daily in healthcare, finance, and management, we examine the disconnect between organizational expectations and worker experiences. We identify key barriers, including poor usability and interoperability, misaligned expectations, limited control, and insufficient communication. These challenges highlight a gap between how organizations implement AI and the evolving worker needs, tasks, and workflows that it fails to support. We argue that successful adoption requires recognizing workers as central to AI integration and propose adaptation strategies at the individual, task, and organizational levels to better align AI systems with real-world practices.

Existing work on AI openness has focused on defining what technical components or release practices qualify a system as "open". However, less is known about how openness is understood and put into practice by people who adopt and adapt these models under real-world constraints. In this paper, we present an empirical study of r/LocalLLaMA, a large online community centered on running and customizing open foundation models locally. Through thematic analysis of community discussions, we find that members conceptualize openness pragmatically - in relation to reliability, local control, privacy, and the ability to adapt models under constraints such as compute resources, licensing, and usability. We identify key motivations for adopting open models, including autonomy, experimentation, and resistance to platform instability, as well as deterrents such as steep learning curves and performance gaps compared to closed systems. We further describe how shared resources and projects, including datasets, evaluation frameworks, and inference tools, sustain interdependent development in the broader open AI ecosystem beyond individual model releases. We then discuss the implications of a utility-oriented view of openness, and how producer support for downstream usability and infrastructure could better enable sustained innovation in open model ecosystems.

When working on digital devices, people often face distractions that can lead to a decline in productivity and efficiency, as well as negative psychological and emotional impacts. To address this challenge, we introduce a novel Artificial Intelligence (AI) assistant that elicits a user's intention, assesses whether ongoing activities are in line with that intention, and provides gentle nudges when deviations occur. The system leverages a large language model to analyze screenshots, application titles, and URLs, issuing notifications when behavior diverges from the stated goal. Its detection accuracy is refined through initial clarification dialogues and continuous user feedback. In a three-week, within-subjects field deployment with 22 participants, we compared our assistant to both a rule-based intent reminder system and a passive baseline that only logged activity. Results indicate that our AI assistant effectively supports users in maintaining focus and aligning their digital behavior with their intentions. Our source code is publicly available at https://intentassistant.github.io

Making AI explainable requires more than algorithmic transparency: it demands understanding who needs explanations and why. In our sixth CHI workshop on Human-Centered XAI (HCXAI), we shift focus to agentic AI systems. LLM-based agents foundationally challenge existing explainability paradigms. Unlike traditional AI that produces single outputs, agents plan multi-step strategies, invoke tools with real-world consequences, and coordinate with other systems; yet current XAI approaches fail to address these complexities. Users need to understand not just what an agent might do, but the cascade of actions it could trigger, the risks involved, and why responses take time. Even our expanded HCXAI frameworks struggle with these new demands. Through our workshop series, we have built a strong community making important conceptual, methodological, and technical impact. This year, we re-examine what human-centered explainable AI means in the agentic era, bringing together researchers and practitioners to shape explainability for both users and developers of these systems.

Algorithmic management (AM)'s impact on worker well-being has led to calls for regulation. However, little is known about the effectiveness and challenges in real-world AM regulation across the regulatory process -- rule operationalization, software use, and enforcement. Our multi-stakeholder study addresses this gap within workplace scheduling, one of the few AM domains with implemented regulations. We interviewed 38 stakeholders across the regulatory process: regulators, defense attorneys, worker advocates, managers, and workers. Our findings suggest that the efficacy of AM regulation is influenced by: (i) institutional constraints that challenge efforts to encode law into AM software, (ii) on-the-ground use of AM software that shapes its ability to facilitate compliance, (iii) mismatches between software and regulatory contexts that hinder enforcement, and (iv) unique concerns that software introduces when used to regulate AM. These findings underscore the importance of a sociotechnical approach to AM regulation, which considers organizational and collaborative contexts alongside the inherent attributes of software. We offer future research directions and implications for technology policy and design.

City governments in the United States are increasingly pressured to adopt emerging technologies. Yet, these systems often risk biased and disparate outcomes. Scholars studying public sector technology design have converged on the need to ground these systems in the goals and organizational contexts of employees using them. We expand our understanding of employees' contexts by focusing on the equity practices of city government employees to surface important equity considerations around public sector data and technology use. Through semi-structured interviews with thirty-six employees from ten departments of a U.S. city government, our findings reveal challenges employees face when operationalizing equity, perspectives on data needs for advancing equity goals, and the design space for acceptable government technology. We discuss what it looks like to foreground equity in data use and technology design, and considerations for how to support city government employees in operationalizing equity with and without official equity offices.

In crowdfunding, the disclosure of campaign details and progress is widely considered beneficial to fundraising because it can resolve information asymmetry and improve operational transparency. However, does this mean that fundraisers should frequently post updates? In this study, we aim to understand the implications of update frequency and content for fundraising in the context of medical crowdfunding. We deploy various natural language processing models to analyze the type, volume, and novelty of the information disclosed in updates and how funders perceive them. Our results delineate an inverted U-shaped relationship between update frequency and donations, contingent on content type. Specifically, we found that medical updates can resolve concerns and foster positive impressions among funders, which improves fundraising. However, as the update frequency increases, further posts may contribute to cognitive overload, attenuating the effect. In contrast, nonmedical updates are associated with a lower donation amount, as this type of update tends to contain redundant information and is negatively received by funders. This study contributes to crowdfunding literature by uncovering the unintended consequences of updates on fundraising due to reduced novelty and information overload. Our results suggest that theorizing the role of updates in medical crowdfunding needs to account for the two-sided nature of information disclosure. In particular, posting novel, issue-relevant information can initially reduce uncertainty and stimulate trust, positively influencing funding outcomes. However, overly frequent updates may include excessive details or repetitive content, adversely impacting fundraising.

The wide adoption of platformized work has generated remarkable advancements in the labor patterns and mobility of modern society. Underpinning such progress, gig workers are exposed to unprecedented challenges and accountabilities: lack of data transparency, social and physical isolation, as well as insufficient infrastructural safeguards. Gig2Gether presents a space designed for workers to engage in an initial experience of voluntarily contributing anecdotal and statistical data to affect policy and build solidarity across platforms by exchanging unifying and diverse experiences. Our 7-day field study with 16 active workers from three distinct platforms and work domains showed existing affordances of data-sharing: facilitating mutual support across platforms, as well as enabling financial reflection and planning. Additionally, workers envisioned future use cases of data-sharing for collectivism (e.g., collaborative examinations of algorithmic speculations) and informing policy (e.g., around safety and pay), which motivated (latent) worker desiderata of additional capabilities and data metrics. Based on these findings, we discuss remaining challenges to address and how data-sharing tools can complement existing structures to maximize worker empowerment and policy impact.

A content creator's success depends on understanding their audience, but existing tools fail to provide in-depth insights and actionable feedback necessary for effectively targeting their audience. We present Proxona, an LLM-powered system that transforms static audience comments into interactive, multi-dimensional personas, allowing creators to engage with them to gain insights, gather simulated feedback, and refine content. Proxona distills audience traits from comments, into dimensions (categories) and values (attributes), then clusters them into interactive personas representing audience segments. Technical evaluations show that Proxona generates diverse dimensions and values, enabling the creation of personas that sufficiently reflect the audience and support data grounded conversation. User evaluation with 11 creators confirmed that Proxona helped creators discover hidden audiences, gain persona-informed insights on early-stage content, and allowed them to confidently employ strategies when iteratively creating storylines. Proxona introduces a novel creator-audience interaction framework and fosters a persona-driven, co-creative process.

AI expansion has accelerated workplace adoption of new technologies. Yet, it is unclear whether and how knowledge workers are supported and trained to safely use AI. Inadequate training may lead to unrealized benefits if workers abandon tools, or perpetuate biases if workers misinterpret AI-based outcomes. In a workshop with 39 workers from 26 countries specializing in human resources, labor law, standards creation, and worker training, we explored questions and ideas they had about safely adopting AI. We held 17 follow-up interviews to further investigate what skills and training knowledge workers need to achieve safe and effective AI in practice. We synthesize nine training topics participants surfaced for knowledge workers related to challenges around understanding what AI is, misinterpreting outcomes, exacerbating biases, and worker rights. We reflect how these training topics might be addressed under different contexts, imagine HCI research prototypes as potential training tools, and consider ways to ensure training does not perpetuate harmful values.

Algorithmic Management (AM), which refers to technologies that use algorithms to oversee and direct workers, is increasingly being introduced across various sectors and workplaces. While previous research has focused on AM's impact on job quality in platform work, its effects on worker well-being in non-platform workplaces remain underexplored. This study seeks to deepen our understanding of the impact of AM on occupational health within non-platform workplaces. Drawing on the socio-technical lens and the Pressure, Disorganization and Regulatory Failure (PDR) model (Quinlan et al., 2001), it aims to identify organizational practices that shape the interplay between AM and employees' work experiences, health, and overall well-being. We conducted a comparative case study with two Swedish logistics companies and collected data from observations and semi-structured interviews. Our analysis focused on the interplay between organizational practices, AM technology, and worker experiences to understand key differences between the cases. Workers at both sites reported a low sense of autonomy and task significance. However, physical and psychological strain from AM was more pronounced in the e-commerce company, a disparity potentially explained by factors of the PDR model. We identified organizational practices that appear to positively influence workers' AM experiences: i) involving workers with the AM technology; ii) integrating AM considerations into occupational safety and health management; iii) designing AM applications that allow worker control; and iv) managerial practices that add qualitative assessments to AM's quantitative evaluations. Our research highlights the critical importance of designing organizational practices that incorporate AM in ways that promote occupational health alongside operational efficiency.

Local and federal agencies are rapidly adopting AI systems to augment or automate critical decisions, efficiently use resources, and improve public service delivery. AI systems are being used to support tasks associated with urban planning, security, surveillance, energy and critical infrastructure, and support decisions that directly affect citizens and their ability to access essential services. Local governments act as the governance tier closest to citizens and must play a critical role in upholding democratic values and building community trust especially as it relates to smart city initiatives that seek to transform public services through the adoption of AI. Community-centered and participatory approaches have been central for ensuring the appropriate adoption of technology; however, AI innovation introduces new challenges in this context because participatory AI design methods require more robust formulation and face higher standards for implementation in the public sector compared to the private sector. This requires us to reassess traditional methods used in this space as well as develop new resources and methods. This workshop will explore emerging practices in participatory algorithm design - or the use of public participation and community engagement - in the scoping, design, adoption, and implementation of public sector algorithms.

Increasing evidence suggests that many deployed AI systems do not sufficiently support end-user interaction and information needs. Engaging end-users in the design of these systems can reveal user needs and expectations, yet effective ways of engaging end-users in the AI explanation design remain under-explored. To address this gap, we developed a design method, called AI-DEC, that defines four dimensions of AI explanations that are critical for the integration of AI systems -- communication content, modality, frequency, and direction -- and offers design examples for end-users to design AI explanations that meet their needs. We evaluated this method through co-design sessions with workers in healthcare, finance, and management industries who regularly use AI systems in their daily work. Findings indicate that the AI-DEC effectively supported workers in designing explanations that accommodated diverse levels of performance and autonomy needs, which varied depending on the AI system's workplace role and worker values. We discuss the implications of using the AI-DEC for the user-centered design of AI explanations in real-world systems.