Generative AI (GenAI) tools improve productivity in knowledge workflows such as writing, but also risk overreliance and reduced critical thinking. Cognitive forcing functions (CFFs) mitigate these risks by requiring active engagement with AI output. As GenAI workflows grow more complex, systems increasingly present execution plans for user review. However, these plans are themselves AI-generated and prone to overreliance, and the effectiveness of applying CFFs to AI plans remains underexplored. We conduct a controlled experiment in which participants completed AI-assisted writing tasks while reviewing AI-generated plans under four CFF conditions: Assumption (argument analysis), WhatIf (hypothesis testing), Both, and a no-CFF control. A follow-up think-aloud and interview study qualitatively compared these conditions. Results show that the Assumption CFF most effectively reduced overreliance without increasing cognitive load, while participants perceived the WhatIf CFF as most helpful. These findings highlight the value of plan-focused CFFs for supporting critical reflection in GenAI-assisted knowledge work.

AI is becoming increasingly integrated into everyday life, both in professional work environments and in leisure and entertainment contexts. This integration requires AI to move beyond acting as an assistant for informational or transactional tasks toward a genuine collaborative partner. Effective collaboration, whether between humans or between humans and AI, depends on establishing and maintaining common ground: shared beliefs, assumptions, goals, and situational awareness that enable coordinated action and efficient repair of misunderstandings. While common ground is a central concept in human collaboration, it has received limited attention in studies of human-AI collaboration. In this paper, we introduce a new benchmark grounded in theories and empirical studies of human-human collaboration. The benchmark is based on a collaborative puzzle task that requires iterative interaction, joint action, referential coordination, and repair under varying conditions of situation awareness. We validate the benchmark through a confirmatory user study in which human participants collaborate with an AI to solve the task. The results show that the benchmark reproduces established theoretical and empirical findings from human-human collaboration, while also revealing clear divergences in human-AI interaction.

The increasing capability of AI models to generate user interfaces has the potential to transform HCI and design practice. We invite researchers, designers, developers, and practitioners to explore how generative UI – interfaces created by AI models – will reshape design methods, workflows, and user experiences. Our goals are to (i) envision how generative UI can underpin innovative human-centric experiences, and (ii) reflect on how HCI and design practice could and should evolve to meet the opportunities and challenges this presents. This will be an interactive and discussion-oriented workshop, featuring a pop-up panel, creative ideation exercises, and collaborative artefact development. Artefacts produced through the workshop will be shared online afterwards and will, we hope, result in an Interactions or CACM article. We will welcome submissions from scholars and practitioners working on dynamic or generative UI, as well as those with expertise in related areas. To keep participation broad, participants will be asked to submit a two-page position paper (in ACM single column format), a two-page pictorial, or a two-minute video at the workshop website. We expect approximately 35 participants to register and attend, including the organizers.

Knowledge can't be disentangled from people. As AI knowledge systems mine vast volumes of work-related data, the knowledge that's being extracted and surfaced is intrinsically linked to the people who create and use it. When these systems get embedded in organizational settings, the information that is brought to the foreground and the information that's pushed to the periphery can influence how individuals see each other and how they see themselves at work. In this paper, we present the looking-glass metaphor and use it to conceptualize AI knowledge systems as systems that reflect and distort, expanding our view on transparency requirements, implications and challenges. We formulate transparency as a key mediator in shaping different ways of seeing, including seeing into the system, which unveils its capabilities, limitations and behavior, and seeing through the system, which shapes workers' perceptions of their own contributions and others within the organization. Recognizing the sociotechnical nature of these systems, we identify three transparency dimensions necessary to realize the value of AI knowledge systems, namely system transparency, procedural transparency and transparency of outcomes. We discuss key challenges hindering the implementation of these forms of transparency, bringing to light the wider sociotechnical gap and highlighting directions for future Computer-supported Cooperative Work (CSCW) research.

Organisations generate vast amounts of information, which has resulted in a long-term research effort into knowledge access systems for enterprise settings. Recent developments in artificial intelligence, in relation to large language models, are poised to have significant impact on knowledge access. This has the potential to shape the workplace and knowledge in new and unanticipated ways. Many risks can arise from the deployment of these types of AI systems, due to interactions between the technical system and organisational power dynamics. This paper presents the Consequence-Mechanism-Risk framework to identify risks to workers from AI-mediated enterprise knowledge access systems. We have drawn on wide-ranging literature detailing risks to workers, and categorised risks as being to worker value, power, and wellbeing. The contribution of our framework is to additionally consider (i) the consequences of these systems that are of moral import: commodification, appropriation, concentration of power, and marginalisation, and (ii) the mechanisms, which represent how these consequences may take effect in the system. The mechanisms are a means of contextualising risk within specific system processes, which is critical for mitigation. This framework is aimed at helping practitioners involved in the design and deployment of AI-mediated knowledge access systems to consider the risks introduced to workers, identify the precise system mechanisms that introduce those risks and begin to approach mitigation. Future work could apply this framework to other technological systems to promote the protection of workers and other groups.

Innovations in machine learning are enabling organisational knowledge bases to be automatically generated from working people's activities. The potential for these to shift the ways in which knowledge is produced and shared raises questions about what types of knowledge might be inferred from working people's actions, how these can be used to support work, and what the broader ramifications of this might be. This article draws on findings from studies of (i) collaborative actions, and (ii) knowledge actions, to explore how these actions might (i) inform automatically generated knowledge bases, and (ii) be better supported through technological innovation. We triangulate findings to develop a framework of actions that are performed as part of everyday work, and use this to explore how mining those actions could result in knowledge being explicitly and implicitly contributed to a knowledge base. We draw on these possibilities to highlight implications and considerations for responsible design.

The COVID-19 pandemic has accelerated digital transformations across industries, but also introduced new challenges into workplaces, including the difficulties of effectively socializing with colleagues when working remotely. This challenge is exacerbated for new employees who need to develop workplace networks from the outset. In this paper, by analyzing a large-scale telemetry dataset of more than 10,000 Microsoft employees who joined the company in the first three months of 2022, we describe how new employees interact and telecommute with their colleagues during their “onboarding” period. Our results reveal that although new hires are gradually expanding networks over time, there still exists significant gaps between their network statistics and those of tenured employees even after the six-month onboarding phase. We also observe that heterogeneity exists among new employees in how their networks change over time, where employees whose job tasks do not necessarily require extensive and diverse connections could be at a disadvantaged position in this onboarding process. By investigating how web-based people recommendations in organizational knowledge base facilitate new employees naturally expand their networks, we also demonstrate the potential of web-based applications for addressing the aforementioned socialization challenges. Altogether, our findings provide insights on new employee network dynamics in remote and hybrid work environments, which may help guide organizational leaders and web application developers on quantifying and improving the socialization experiences of new employees in digital workplaces.

We are delighted to present this issue of the Proceedings of the ACM on Human-Computer Interaction, which contains scholarship from the Computer-Supported Cooperative Work and Social Computing (CSCW) community. This issue has 293 papers, 94 that were accepted from the April 2021 cycle, 61 that were accepted from the July 2021 cycle, and 138 that were accepted from the January 2022 cycle. It reflects great efforts and contributions from external reviewers, Associate Chairs and Editors, who together have conducted a rigorous review process. As Papers Chairs, we are grateful for the community's collective efforts to continue shaping and sharing CSCW's tradition of high-quality scholarship during an ongoing global pandemic.

Online freelance platforms can transform knowledge work. However, 'gigification' also presents challenges, including how freelance workers can access and work with knowledge, which prior research has not examined. Through a qualitative interview study, we identify disparities in how freelancers who work for enterprise companies are able to utilize knowledge as part of their work, when compared with traditional employees of similar organizations. We examine how 38 knowledge workers (21 freelancers, 17 employees) deploy knowledge, work skillfully and mobilize resources to meet knowledge needs. We find that both employees and freelancers understand their own ability to act knowledgeably as a dynamic, collaborative, negotiated and emergent accomplishment. However, for freelancers, the dynamic dimensions of knowledge work - such as helping others see the meaning and value of their work, and creating ties between their work and the enterprise - are only minimally-legitimized and minimally-supported by organizing structures and tools. We present our results as 'knowledge gaps', and propose design recommendations to reduce these gaps and consequently make on-demand knowledge work more effective and sustainable.

Organizational knowledge bases are moving from passive archives to active entities in the flow of people's work. We are seeing machine learning used to enable systems that both collect and surface information as people are working, making it possible to bring out connections between people and content that were previously much less visible in order to automatically identify and highlight experts on a given topic. When these knowledge bases begin to actively bring attention to people and the content they work on, especially as that work is still ongoing, we run into important challenges at the intersection of work and the social. While such systems have the potential to make certain parts of people's work more productive or enjoyable, they may also introduce new workloads, for instance by putting people in the role of experts for others to reach out to. And these knowledge bases can also have profound social consequences by changing what parts of work are visible and, therefore, acknowledged. We pose a number of open questions that warrant attention and engagement across industry and academia. Addressing these questions is an essential step in ensuring that the future of work becomes a good future for those doing the work. With this position paper, we wish to enter into the cross-disciplinary discussion we believe is required to tackle the challenge of developing recommender systems that respect social values.

In this paper we present findings from a bibliometric evaluation of scientific publications on human-AI systems, indexed in the Dimensions database over the past five years (2018 to 2022). The study maps the research landscape in this burgeoning area, as it relates to the topic of collaboration. To this end, we assessed publication and citation counts over time, authorship-level indicators, and keyword occurrence frequency. We also examined funding information as an indicator of research priorities, alongside usage-based statistics and alternative metrics such as social media mentions, recommendations, and reads. Our preliminary findings highlight a significant focus on aspects like trust, explainability, transparency, and autonomy in highly complex scenarios through the use of generative models and hybrid interaction techniques. The results also reveal a growth in the number of publications and funding grants, although a certain lack of maturity is observable in terms of citation patterns and coherence of thematic clusters.

Automation has been permeating our everyday lives in various facets. Given both the ubiquity and, in many cases, the indispensability of ubiquitous automated systems, creating engaging experiences with them becomes increasingly relevant. This workshop provides a platform for researchers and practitioners working on (semi-)automated systems and their user experience and allows for cross-discipline networking and knowledge transfer. In a keynote talk, paper presentations, discussions, and hands-on sessions, the participants will explore and discuss user engagement with automation for operation, appropriation, and change. The results of the workshop are a set of research ideas and drafts of joint research projects to drive further automation experience research in a collaborative interdisciplinary manner.

In this article, we introduce an affordance-orientated approach for the study of digital possessions. We identify affordances as a source of value for digital possessions and argue that dominant meaning-orientated approaches do not enable us to fully appreciate these sources of value. Our work recognizes that value is released and experienced in "the doing"-people must do things with digital objects to locate and obtain value in and from them. We distinguish three levels of affordance for digital possessions-low, mid, and high-and introduce the concept of digital incorporation to explain how the three levels of affordances come together, with the individual's own intentionality to enable the achievement of goals. We draw from postphenomenological interviews with 47 individuals in the UK to provide a possession-based and lived experience approach to affordances that sheds new light on their vital role in everyday life and goals.

Innovations in machine learning are enabling organisational knowledge bases to be automatically generated from employees’ activities, the results of which can then be presented to workers via the software applications they commonly use. The potential for these systems to shift the ways in which knowledge is produced and shared raises questions regarding what types of knowledge might be inferred from employees’ practices, how these can be used to support work, and what the broader ramifications of this might be. This paper draws on findings from two studies to offer an initial exploration of these topics. The research described investigated workplace (i) collaborative actions and (ii) knowledge actions, to explore how they might (i) inform automatically generated knowledge bases, and (ii) find support through the design of intelligent systems. We draw on the literature on implicit interactions in considering next steps.

We are delighted to present this issue of the Proceedings of the ACM on Human-Computer Interaction, which contains scholarship from the Computer-Supported Cooperative Work and Social Computing (CSCW) community. This issue has 190 papers, 177 submitted in June 2020 and 13 submitted in October 2020. It represents contributions from two Program Committees, including external reviewers, Associate Chairs, and Editors, who together have conducted a rigorous review process. As Papers Chairs, we are grateful for the community's collective efforts to continue shaping and sharing CSCW's tradition of high-quality scholarship during a global pandemic.

We present Enterprise Alexandria, a new system for automatically constructing a knowledge base with high-precision and typed entities from private enterprise data such as emails, documents and intranet pages. Built as an extension of Alexandria [Winn et al., 2019], the key novelty of Enterprise Alexandria is the ability in processing both the textual information and the structured metadata available in each document in an online learning fashion, making use of any manual curations that have happened in the interim. This task is performed entirely eyes-off to respect the privacy of the user and the restricted access their documents. The knowledge discovery process uses a probabilistic program defining the process of generating the data item from a set of unknown typed entities. Using probabilistic inference, Enterprise Alexandria can jointly discover a large set of entities with custom types specific to the organization. Experiments on three real-world datasets show that the system outperforms alternative methods with the ability to work effectively at large scale.