Awareness technologies are a core interest for CSCW: When people pay attention to each other's actions, it reduces the need for active communication required to accomplish the complex, cooperative work, for example, characteristic of hospitals. In Denmark, healthcare workers (HCWs) and patients face challenges with the architectural design of super hospitals. Change of workflows – most importantly, the shift to single-patient rooms in the new super hospitals - is restricted by the fact that HCWs' well-being is at risk when they have to attend to more rooms. Also, the risk of adverse events increases. The patient's well-being is at risk when they feel lonely or even forgotten in a single-patient room. In this paper, we propose an agenda for awareness technologies designed around both worker- and patient well-being. Our proposal is examined through prototyping an awareness technology, iAware. The solution draws together insights from a long-term ethnographic study (+5y) of how to design sensed environments responsibly [Anon]. Today's awareness technologies in hospitals are typically designed from HCWs' perspective. We identified 4 openings for supporting patients' and HCWs' mutual awareness of workflows: 1) progress of 'ward rounds', 2) patient 'visits', 3) patient 'calls', and 4) patient 'mobility.' We end with concluding remarks on how sensed environments can be designed with an agenda of being relevant to HCWs and patients' well-being.

Abstract Scholars across Computer-Supported Cooperative Work (CSCW) increasingly focus on the topic of care when investigating data-driven technologies in contexts of re-humanizing technology design and usage. Previous studies have shown how care work eludes complex bureaucratic systems shaped by data, digitalization, and a restrictive political agenda. This research aims to understand how asylum stakeholders enact care as an aspect of asylum casework, while navigating what is largely acknowledged by NGOs, nation states, and the EU to be a broken asylum system (von der Leyen). We investigate care as a relational aspect of casework in which knowledge and technology of the implicated caseworker and asylum seeker are attuned to one another in a way that takes the unaccountable into account (following Mol 2010). We add to studies of care in CSCW by empirically expanding the research sites of care and data work. In this multi-sited ethnographically informed study, we conducted interviews (n = 19) and 160 h of observational studies amongst: 1) Danish Red Cross care workers; 2) Danish Refugee Council legal counsellors; and 3) Danish Immigration Service case officers. We contribute empirically grounded insights into the meanings of care in a datafied asylum context. We find that care is enacted by caseworkers in moments of ambivalence, translation, and attentiveness to “new substantial information” relevant for asylum decision-making. We find that these relational aspects of care in asylum casework impact the production of data about the asylum seeker. We end with a discussion of how a care perspective increases our sensitivity as CSCW researchers towards understanding the conditions for producing quality data and documentation in casework.

Public authorities make decisions that greatly impact both citizens and non-citizens. Decision-making on asylum, which is regulated by international law but administered by states, in particular is characterised by a higher level of secrecy than other public services. The 1951 Refugee Convention defnes refugeehood as the fear of being persecuted for reasons of race, religion, nationality, social group, or political opinion. Although fear of gender-related persecution was not included as one of the grounds meriting asylum, state practice means that it is today generally recognised as such. The United Nations Refugee Agency (UNHCR) recommends that states "ensure a gender-sensitive interpretation of the 1951 Refugee Convention." Using natural language processing (NLP) to analyse an open dataset of Danish asylum case summaries, we frst identify fve empirical categories connected to gender in the case summaries: 1) gender-related persecution, 2) LGBT 3) sexual conditions, 4) marital conditions and 5) other gender-related forms of persecution. Secondly, we illustrate the relationship between these gender-related categories and other categories/topics in asylum motives. Finally, we discuss how data science techniques can be applied to better understand complex, cooperative work practices in an area where access for researchers is limited, but archival data is available.

For over a quarter century, GROUP has offered a premier yet intimate and welcoming venue for agenda-setting, diverse research. Although the traditional focus of the conference is on supporting group work, it has expanded to include research from computer-supported cooperative work, sociotechnical studies, practice-centered computing, human-computer interaction, computersupported collaborative learning, participatory technology design, and other related areas. The work presented in this issue embodies that interdisciplinary ethos. Papers in this issue cover a wide range topics, from human-AI collaboration, to collaboration in virtual reality, to perceptions of privacy and security, to the myriad impacts of the COVID-19 pandemic. The application domains are similarly wide ranging, from health data, to civic engagement, to educational settings, to government provision of social services. Similar to the 2021 issue, this issue also continues the tradition of design fiction at GROUP. This issue of PACM:HCI brings you papers from the planned 2022 ACMConference on Supporting Group Work (GROUP 2022). Typically, the GROUP conference occurs every two years. However, research developments do not necessarily follow conference deadline cycles. Thus, the GROUP conference offers authors the opportunity to submit to multiple waves. The first wave of papers for this conference were published in July 2021 in Volume 5 of PACM:HCI, and papers from this current issue were first submitted in May 2021. Both of these sets of papers published as part of the planned GROUP 2022 conference were authored and reviewed during the COVID-19 pandemic. These papers represent commendable volumes of hard work and resilience, not just from the authors, but also from the reviewers, the program committee, and the conference organizers. Additionally, the pandemic forced a major change to the conference at which these papers will be presented.

In asylum decision-making, legal authorities rely on the criterion "credibility" as a measure for determining whether an individual has a legitimate asylum claim; that is, whether they have a well-founded fear of persecution upon returning to their country of origin. Nation states, international institutions, and NGOs increasingly seek to leverage data-driven technologies to support such decisions, deploying processes of data cleaning, contestation, and interpretation. We qualitatively analyzed 50 asylum cases to understand how the asylum decision-making process in Denmark leverages data to configure individuals as credible (or not). In this context, data can vary from the applicant's testimony to data acquired on the applicant from registers and alphanumerical data. Our findings suggest that legal authorities assess credibility through a largely discretionary practice, establishing certainty by ruling out divergence or contradiction between the different forms of data and documentation involved in an asylum case. As with other reclassification processes [following Bowker and Star 1999], credibility is an ambiguous prototypical concept for decision-makers to attempt certainty, especially important to consider in the design of data-driven technologies where stakeholders have differential power.

As more and more governments adopt algorithms to support bureaucratic decision-making processes, it becomes urgent to address issues of responsible use and accountability. We examine a contested public service algorithm used in Danish job placement for assessing an individual's risk of long-term unemployment. The study takes inspiration from cooperative audits and was carried out in dialogue with the Danish unemployment services agency. Our audit investigated the practical implementation of algorithms. We find (1) a divergence between the formal documentation and the model tuning code, (2) that the algorithmic model relies on subjectivity, namely the variable which focus on the individual's self-assessment of how long it will take before they get a job, (3) that the algorithm uses the variable "origin" to determine its predictions, and (4) that the documentation neglects to consider the implications of using variables indicating personal characteristics when predicting employment outcomes. We discuss the benefits and limitations of cooperative audits in a public sector context. We specifically focus on the importance of collaboration across different public actors when investigating the use of algorithms in the algorithmic society.

Public services increasingly presume a new and more active role for individuals to play in datafied society. While design efforts increasingly attempt to include stakeholders, such attempts are often limited to professional perspectives. Little is known about how individuals who are subject to these solutions experience the increasing use of data about them. One example of public services is job placement. Taking design fiction as our approach, we invited individuals enrolled in job placement (n=20) to reflect on the ‘work of the unemployed’, a fictive scenario where individuals make themselves eligible for support through sharing data. The fiction addresses power dynamics. The study shows how approaches, such as design fiction, are effective at including marginalized communities through changing the conditions for design. Showcasing the fictional outlook, and how the ‘design experience’ can be disempowering if not qualified through a deeper critique, the paper contributes to agendas on design justice.

The research outlined in this paper is focusing on understanding asylum decision-making and outcome variations across the Nordic countries. As a preliminary study, we extract data from an open dataset1 of decision summaries from the Danish Refugee Appeals Board. The approach we propose, offers points for discussion of how prototyping context and participation can help raise questions about such data and engage stakeholders. Combining the application of Machine Learning (ML) and Natural Language Processing (NLP) with participatory methods (e.g. critically designed artefacts) enable us to 1) move beyond “obvious” ML-application areas, 2) through sensemaking of data with stakeholders, and 3) co-develop approaches to data science from a CSCW-perspective.

No abstract available.

Studies of algorithmic decision-making in Computer-Supported Cooperative Work (CSCW) and related fields of research increasingly recognize an analogy between AI and bureaucracies. We elaborate this link with an empirical study of AI in the context of decision-making in a street-level bureaucracy: job placement. The study examines caseworkers' perspectives on the use of AI, and contributes to an understanding of bureaucratic decision-making, with implications for integrating AI in caseworker systems. We report findings from a participatory workshop on AI with 35 caseworkers from different types of public services, followed up by interviews with five caseworkers specializing in job placement. The paper contributes an understanding of caseworkers' collaboration around documentation as a key aspect of bureaucratic decision-making practices. The collaborative aspects of casework are important to show because they are subject to process descriptions making case documentation prone for an individually focused AI with consequences for the future of how casework develops as a practice. Examining the collaborative aspects of caseworkers' documentation practices in the context of AI and (potentially) automation, our data show that caseworkers perceive AI as valuable when it can support their work towards management, (strengthen their cause, if a case requires extra resources), and towards unemployed individuals (strengthen their cause in relation to the individual's case when deciding on, and assigning a specific job placement program). We end by discussing steps to support cooperative aspects in AI decision-support systems that are increasingly implemented into the bureaucratic context of public services.

People are increasingly subject to the tracking of data about them at their workplaces. Sensor tracking is used by organizations to generate data on the movement and interaction of their employees to monitor and manage workers, and yet this data also poses significant risks to individual employees who may face harms from such data, and from data errors, to their job security or pay as a result of such analyses. Working with a large hospital, we developed a set of intervention strategies to enable what we call "collective sensemaking" describing worker contestation of sensor tracking data. We did this by participating in the sensor data science team, analyzing data on badges that employees wore over a two-week period, and then bringing the results back to the employees through a series of participatory workshops. We found three key aspects of collective sensemaking important for understanding data from the perspectives of stakeholders: 1) data shadows for tempering possibilities for design with the realities of data tracking; 2) data transducers for converting our assumptions about sensor tracking, and 3) data power for eliciting worker inclusivity and participation. We argue that researchers face what Dourish (2019) called the "legitimacy trap" when designing with large datasets and that research about work should commit to complementing data-driven studies with in-depth insights to make them useful for all stakeholders as a corrective to the underlying power imbalance that tracked workers face.

This paper presents a design fiction: a research prototype of a platform for unemployed individuals trading their personal data. The design fiction questions the ramifications of an understanding of data as individual property by showing a near-future speculative scenario of what government-driven job placement could look like. Which are the kinds of accountability and agency that could be leveraged in the context of job placement if data of unemployed individuals are considered property that can be traded with the public sector in return for support? An algorithm classifies the performance of unemployed individuals based on the data they upload. This way the algorithm becomes the central mechanism for accountability and control instead of the caseworker, who acts as an arbitrator between the individual citizen and algorithm. Our purpose with this paper, and the speculative research prototype, is to create a space for reflection on dilemmas in relation to data property, accountability, and agency in public services.

We report on a new approach to co-creating adaptive case management systems jointly with end-users, developed in the context of the Effective co-created and compliant adaptive case Management Systems for Knowledge Workers (EcoKnow.org) research project. The approach is based on knowledge from prior ethnographic field studies and research in the declarative Dynamic Condition Response (DCR) technology for model-driven design of case management systems. The approach was tested in an operational environment jointly with the danish municipality of Syddjurs by conducting a service-design project and implementing an open source case manager tool and a new highlighter tool for mapping between textual specifications and the DCR notation. The design method and technologies were evaluated by understandability studies with end-users. The study showed that the development could be done in just 6 months, and that the new highlighter tool in combination with the traditional design and simulation tools, supports domain experts formalise and provide traceability between their interpretations of textual specifications and the formal models.

This paper empirically investigates a Common Information Space (CIS) established by medical secretaries so they could support each other during their workplace’s transition to a new comprehensive electronic health record, called the Healthcare Platform (HP). With the new system, the secretaries were expected to become partially obsolete, as doctors were to take on a significant load of the clerical work, such as documenting and coding. To handle their changing work situation, the medical secretaries set up an online support group in parallel to, but independent from, the official implementation support organization. The paper’s contribution is a characterization of the support group as a common information space (CIS), and analysis of the specific qualities of a worker-driven CIS as a forum for 1) articulation work required for re-grounding changing tasks and responsibilities, 2) archiving discussions (posts) and guidelines to further their collective interpretation, and 3) creating a space independent of management for employees to work out their new role in an organization in a situation of transition and change.

It is our pleasure to welcome you to the first PACMHCI GROUP issue. For over 25 years, the GROUP research community has supported the development of robust scholarship at the intersection of Computer Supported Cooperative Work, Human Computer Interaction, Computer Supported Collaborative Learning and Socio-Technical Studies. This volume is the latest product of our efforts to validate and integrate the strong work happening within this broadly-conceived community. We hope that the range of papers presented here reflects our intent to be international, interdisciplinary, and inclusive, both in our organization of the review process as well as within the set of accepted papers. This first PACM journal volume in the GROUP series features studies of collaboration in multiple settings, including social networks, editing systems, and mixed reality. It also showcases sociotechnical studies in domains ranging from outdoor activities to scientific projects.

This study examines caseworkers' and citizens' interactions when assembling resource development applications for citizens with serious health and personal issues. As with other types of welfare schemes, the application serves as a mechanism of both support and control. From our study, we illustrate how an increased reliance on data is transforming the citizen-caseworker interaction in social welfare. We characterize this transformation as 'dataRication': a phenomenon where the increased reliance on data for decision-support across contexts of data production makes it challenging for individual citizens to contest or correct data-born accounts of their situation. Our contribution is two-fold: Rirst, we empirically characterize the citizen-caseworker interaction in the application process. Second, we discuss how citizens' private resourcing complements the formal application process and provides them with strategies to give authority to their case and exercise personal autonomy. The private resourcing practices we observed show how integrating supplementary accounts from citizens into the systems that caseworkers rely on could make citizens' experiences and social context legible. This in turn has policy and technology design implications as public services increasingly introduce data-driven modes of case management.

The excitement around computing technology in all aspects of life requires that we tackle fundamental issues of healthcare, leisure, labor, education, and food to create the society we want. The aim of this satellite event was to bring together a variety of different stakeholders, ranging from local food producers, chefs, designers, engineers, data scientists, and sensory scientists, to discuss the interwoven future of computing technology and food. This event was co-located with the AVI 2018 conference and supported by the ACM Future of Computing Academy (ACM-FCA). The event followed a co-creation approach that encourages conjoined creative and critical thinking that feeds into the formulation of a manifesto on the future of computing and food. We hope this will inspire future discussions on the transformative role of computing technology on food.

This paper examines how mobile technology impacts employee accountability in the blue-collar data-driven workplace. We conducted an observation-based qualitative study of how electricians in an electrical company interact with data related to their work accountability, which comprises the information employees feel is reasonable to share and document about their work. The electricians we studied capture data both manually, recording the hours spent on a particular task, and automatically, as their mobile devices regularly track data such as location. First, our results demonstrate how work accountability manifests for employees' manual labor work that has become data-driven. We show how employees work through moments of transparency, privacy, and accountability using data focused on location, identification and time. Second, we demonstrate how this data production is interdependent with employees' beliefs about what is a reasonable level of detail and transparency to provide about their work. Lastly, we articulate specific design implications related to work accountability.

Inclusion in computer science education and profession is a debated topic in recent feminist HCI literature. To enable inclusion in computer science, we must find new ways to create opportunities for broadening the narrative of computing embracing diversity. In this paper, we present GRACE, an interactive installation, which combines history, crafts, and digital technology. The installation creates an opportunity for multiple people to engage into discussions around inclusion in computer science, thus facilitates open discussion of perspectives beyond predominant narratives of computing.

Introducing data tracking of patients, doctors, medicine, or medical equipment holds promise to optimize workflows within hospitals. However, placing tracking devices and collecting data does not automatically translate into new workflows; instead, it requires careful considerations, knowledge about the activities tracked, and technical knowledge. In this paper, we study the interdisciplinary work involved when data tracking techniques are used to construe workflows within a real life work setting: architectural design of hospitals. We provide an ethnographic account from a unique study where two types of data tracking techniques were utilized in an existing hospital with the aim of informing the architectural design of a future hospital. The paper makes three contributions. First, we characterize the work involved in construing workflows based upon data tracking. Second, we discuss surveillance as an empirical category, which emerged in our study. Finally, we argue that to be able to construe workflows based upon data tracking, balancing the seamless boundary of privacy in work and surveillance in cooperation with the practitioners is of critical importance. Without support from the practitioners, data quality is at risk, and the resulting workflows might turn out 'flawed'.

Coordination is central in CSCW systems design, where it is often considered as a process of bringing artifacts and activities together and making them part of a larger system. In this paper, we argue that existing conceptualizations of coordination in CSCW can be successfully extended with the notion of coordination by avoidance. We introduce this notion to describe particular coordination mechanisms whereby actors avoid routines or routes of actions when it conflicts with those of other actors. In a study of pre-diagnostic work, we found that actors coordinate by avoidance when they realize alternative routes of action or that a routine has to be set to a halt to ensure that practices stay coordinated. Routines in diagnostic work are for instance the rescheduling of patients and requesting of relevant patient records that are mundane practices, however, necessary when responsibility is shared or shifts between various actors collaborating to diagnose a patient. Thus, the contribution of this paper lies in empirically identifying practices of avoidance and extending dominant conceptualizations of coordination through the notion of avoidance. We identify four ways that actors coordinate their practices by avoidance; by demarcating, procrastinating, delegating and accommodating routines or routes of action. Furthermore, we conceptualize coordination by avoidance as a distinct type of coordination mechanism to be taken into consideration in CSCW information systems design.