Emergency departments (EDs) often experience overcrowding, staff shortages, and long waiting times, which put a strain on clinicians and negatively impact patients’ experiences. Although AI is commonly proposed to optimize clinical workflows, the perspectives of patients are often overlooked in AI design. We report on a four-phase study at a university hospital combining preparatory fieldwork, co-creation workshops, design phase, and storyboard-guided interviews. Our findings reveal a misalignment: clinicians framed the contribution of AI around triage efficiency, while patients emphasized reassurance, empathy, and guidance. Addressing both needs, we developed and evaluated a concept of an ephemeral AI companion for the ED waiting area, designed to provide orientation, support reflection, and prepare patients for consultations without substituting human contact. We contribute: empirical evidence of patients’ needs, concerns, and expectations for AI support, design principles for an ephemeral AI companion concept, and findings from a study conducted with patients during their ED waits.

Robot-assisted surgery (RAS) has raised concerns within human-computer interaction, particularly regarding the socio-material configurations of robotic systems and their impact on surgical practices. To investigate these configurations in the context of the da Vinci robotic system, we used a research-through-design approach. Through this approach, we developed six Visual RAS (ViRAS) scenarios derived from RAS observations. These scenarios represent different configurations of interacting surgical team members and the robotic system in distinct adverse RAS events. In ViRAS-guided interviews with experienced RAS surgeons, we found that ViRAS scenarios help reflect on surgical practices and the specific material and spatial properties of RAS. Our findings indicated that the team’s cognitive engagement during surgery could be improved by providing sensory augmentation to facilitate task perception and individual skill development. Through our research, we show how ViRAS scenarios, as a tool for reflection, can reveal opportunities for designing socio-material configurations in RAS and beyond.

In the digital health domain, ethical data collection practices are crucial for ensuring the availability of quality datasets that drive medical advancement. Data donation, allowing patients to share their medical data for secondary research purposes, presents a promising resource for such datasets. Yet, current consent user interfaces mediating data-sharing decisions are found to favor data collectors' values over those of data subjects. Seeking to establish value-centered data collection practices in digital health, we investigate the design of consent user interfaces that support end-users in making value-congruent data-sharing decisions. Focusing our research efforts on the situated context of health data donation at the psychosomatic unit of a university hospital, we demonstrate how a human-centered design can ground technology within the perspective of a vulnerable group. We employed an exploratory sequential mixed-method approach consisting of five phases: (1) Participatory workshops elicit patient values, informing the (2) design of a proposed Value-Centered Consent Interface . An (3) online experiment demonstrates our interface element's effect, increasing value congruence in data-sharing decisions. Our proposed consent user interface design is then adapted to the research context through a (4) co-creation workshop with domain experts and (5) a user interface evaluation with patients. Our work contributes to recent discourse in CSCW concerning ethical implications of new data practices within their socio-technological context by exploring patient values on medical data-sharing, introducing a novel consent interface leveraging reflection to support value-congruent decision-making, and providing a situated evaluation of the proposed consent user interface with patients.

As AI becomes increasingly relevant, especially in high-stakes domains such as healthcare, it is important to investigate which approaches can improve human-AI collaboration and, if so, why. Current research focuses primarily on technically available approaches, such as explainable AI (XAI), often overlooking human needs. This study bridges this gap by adopting a well-established technical approach - model uncertainty representations - by considering users' familiarity with the format and numeracy skills. Despite being provided with uncertainty representations, users may still struggle to handle uncertain decisions. Thus, we introduce an educational approach that communicates the capabilities of humans and the AI system to users, supplementing the uncertainty representations. We conducted a pre-registered, between-subjects user study to determine whether these approaches resulted in improved human-AI team performance, mediated by the user's mental model of the AI. Our findings indicate that solely providing uncertainty representations does not improve team performance or the user's mental model in comparison to only providing AI recommendations. However, incorporating capability-focused guidance alongside uncertainty representations significantly enhances correct self-reliance and, to some extent, overall team performance. Our additional exploratory analyses suggest that factors such as task uncertainty, case difficulty, and case type, rather than numeracy skills, the need for cognition or familiarity, can influence team performance. We discuss these factors in detail, provide practical implications, and suggest directions for further research. This work contributes to the CSCW discourse by demonstrating how technical approaches can be augmented with educational approaches to enhance human-AI collaboration in decision-making tasks.

Data collections, such as those from citizen science projects, can provide valuable scientific insights or help the public to make decisions based on real demand. At the same time, the collected data might cause privacy risks for their volunteers, for example, by revealing sensitive information. Similar but less apparent trade-offs exist for data collected while using social media or other internet-based services. One approach to addressing these privacy risks might be to anonymize the data, for example, by using Differential Privacy (DP). DP allows for tuning and, consequently, communicating the trade-off between the data contributors' privacy and the resulting data utility for insights. However, there is little research that explores how to communicate the existing trade-off to users. % We contribute to closing this research gap by designing interactive elements and visualizations that specifically support people's understanding of this privacy-utility trade-off. We evaluated our user interfaces in a user study (N=378). Our results show that a combination of graphical risk visualization and interactive risk exploration best supports the informed decision, \ie the privacy decision is consistent with users' privacy concerns. Additionally, we found that personal attributes, such as numeracy, and the need for cognition, significantly influence the decision behavior and the privacy usability of privacy decision interfaces. In our recommendations, we encourage data collectors, such as citizen science project coordinators, to communicate existing privacy risks to their volunteers since such communication does not impact donation rates. %Understanding such privacy risks can also be part of typical training efforts in citizen science projects. %DP allows volunteers to balance their privacy concerns with their wish to contribute to the project. From a design perspective, we emphasize the complexity of the decision situation and the resulting need to design with usability for all population groups in mind. % We hope that our study will inspire further research from the human-computer interaction community that will unlock the full potential of DP for a broad audience and ultimately contribute to a societal understanding of acceptable privacy losses in specific data contexts.

The secondary use of routinely collected patient data made possible by the broad consent form is seen as a prerequisite for developing data-driven health technologies. In Germany, relevant stakeholder groups (e.g., ethics committees and data protection authorities) specified the broad consent form; however, only one group of patient representatives was consulted, which may indicate asymmetries in engagement. This situation informed our research on medical data donation and emphasized foregrounding patient values. Drawing on participatory design, value sensitive design, and emerging research on value-led participation, we propose a method consisting of (1) a workshop concept for participatory value elicitation composed of four carefully coordinated phases and (2) an analysis procedure to examine the empirical data collected. This analysis allowed us to derive design requirements for medical data donation user interfaces. We conducted three workshops with patient advocates of vulnerable groups and patients in residential care of a psychosomatic unit. Our findings provide new directions to improve user interfaces for medical data donation: First, user interfaces need to enhance patients' reflective thinking about the potential consequences of their data donation; second, a decision facilitator supporting patients' value-based decision-making (e.g., by providing simple language or tailoring descriptions to patient needs); and finally, a data intermediary relieving patients' decision-making and giving them control over their data after donation. Moreover, we emphasize the need to increase the use of participatory approaches in health technology development.

Just as other disciplines, the humanities explore how computational research approaches and tools can meaningfully contribute to scholarly knowledge production. Building on related work from the areas of CSCW and HCI, we approach the design of computational tools through the analytical lens of 'human-AI collaboration.' Such work investigates how human competencies and computational capabilities can be effectively and meaningfully combined. However, there is no generalizable concept of what constitutes 'meaningful' human-AI collaboration. In terms of genuinely human competencies, we consider criticality and reflection as guiding principles of scholarly knowledge production and as deeply embedded in the methodologies and practices of the humanities. Although (designing for) reflection is a recurring topic in CSCW and HCI discourses, it has not been centered in work on human-AI collaboration. We posit that integrating both concepts is a viable approach to supporting 'meaningful' human-AI collaboration in the humanities and other qualitative, interpretivist, and hermeneutic research areas. Our research, thus, is guided by the question of how critical reflection can be enabled in human-AI collaboration. We address this question with a use case that centers on computer vision (CV) tools for art historical image retrieval. Specifically, we conducted a qualitative interview study with art historians to explore a) what potentials and affordances art historians ascribe to human-AI collaboration and CV in particular, and b) in what ways art historians conceptualize critical reflection in the context of human-AI collaboration. We extended the interviews with a think-aloud software exploration. We observed and recorded participants' interaction with a ready-to-use CV tool in a possible research scenario. We found that critical reflection, indeed, constitutes a core prerequisite for 'meaningful' human-AI collaboration in humanities research contexts. However, we observed that critical reflection was not fully realized during interaction with the CV tool. We interpret this divergence as supporting our hypothesis that computational tools need to be intentionally designed in such a way that they actively scaffold and support critical reflection during interaction. Based on our findings, we suggest four empirically grounded design implications for 'critical-reflective human-AI collaboration': supporting reflection on the basis of transparency, foregrounding epistemic presumptions, emphasizing the situatedness of data, and strengthening interpretability through contextualized explanations.

Every day, we have to decide multiple times, whether and how much personal data we allow to be collected. This decision is not trivial, since there are many legitimate and important purposes for data collection, for examples, the analysis of mobility data to improve urban traffic and transportation. However, often the collected data can reveal sensitive information about individuals. Recently visited locations can, for example, reveal information about political or religious views or even about an individual's health. Privacy-preserving technologies, such as differential privacy (DP), can be employed to protect the privacy of individuals and, furthermore, provide mathematically sound guarantees on the maximum privacy risk. However, they can only support informed privacy decisions, if individuals understand the provided privacy guarantees. This article proposes a novel approach for communicating privacy guarantees to support individuals in their privacy decisions when sharing data. For this, we adopt risk communication formats from the medical domain in conjunction with a model for privacy guarantees of DP to create quantitative privacy risk notifications.

During a research project in which we developed a machine learning (ML) driven visualization system for non-ML experts, we reflected on interpretability research in ML, computer-supported collaborative work and human-computer interaction. We found that while there are manifold technical approaches, these often focus on ML experts and are evaluated in decontextualized empirical studies. We hypothesized that participatory design research may support the understanding of stakeholders' situated sense-making in our project, yet, found guidance regarding ML interpretability inexhaustive. Building on philosophy of technology, we formulated explanation strategies as an empirical-analytical lens explicating how technical explanations mediate the contextual preferences concerning people's interpretations. In this paper, we contribute a report of our proof-of-concept use of explanation strategies to analyze a co-design workshop with non-ML experts, methodological implications for participatory design research, design implications for explanations for non-ML experts and suggest further investigation of technological mediation theories in the ML interpretability space.

Ensuring the quality of the content provided in online settings is an important challenge today, for example, for social media or news. The Wikipedia community has ensured the high-quality standards for an online encyclopaedia from the beginning and has built a sophisticated set of automated, semi-automated, and manual quality assurance mechanisms over the last fifteen years. The scientific community has systematically studied these mechanisms but one mechanism has been overlooked --- edit filters. Edit filters are syntactic rules that assess incoming edits, file uploads or account creations. As opposed to many other quality assurance mechanisms, edit filters are effective before a new revision is stored in the online encyclopaedia. In the exploratory study presented, we describe the role of edit filters in Wikipedia's quality assurance system. We examine how edit filters work, describe how the community governs their creation and maintenance, and look into the tasks these filters take over. Our goal is to steer researchers' attention to this quality control mechanism by pointing out directions for future studies.

Wikidata, initially developed to serve as a central structured knowledge base for Wikipedia, is now a melting point for structured data for companies, research projects and other peer production communities. Wikidata's community consists of humans and bots, and most edits in Wikidata come from these bots. Prior research has raised concerns regarding the challenges for editors to ensure the quality of bot-generated data, such as the lack of quality control and knowledge diversity. In this research work, we provide one way of tackling these challenges by taking a closer look at the approval process of bot activity on Wikidata. We collected all bot requests, i.e. requests for permissions (RfP) from October 2012 to July 2018. We analyzed these 683 bot requests by classifying them regarding activity focus, activity type, and source mentioned. Our results show that the majority of task requests deal with data additions to Wikidata from internal sources, especially from Wikipedia. However, we can also show the existing diversity of external sources used so far. Furthermore, we examined the reasons which caused the unsuccessful closing of RfPs. In some cases, the Wikidata community is reluctant to implement specific bots, even if they are urgently needed because there is still no agreement in the community regarding the technical implementation. This study can serve as a foundation for studies that connect the approved tasks with the editing behavior of bots on Wikidata to understand the role of bots better for quality control and knowledge diversity.

Interactive intelligent systems, i.e., interactive systems that employ AI technologies, are currently present in many parts of our social, public and political life. An issue reoccurring often in the development of these systems is the question regarding the level of appropriate human and computer contributions. Engineers and designers lack a way of systematically defining and delimiting possible options for designing such systems in terms of levels of automation. In this paper, we propose, apply and reflect on a method for human-computer configuration design. It supports the systematic investigation of the design space for developing an interactive intelligent system. We illustrate our method with a use case in the context of collaborative ideation. Here, we developed a tool for information extraction from idea content. A challenge was to find the right level of algorithmic support, whereby the quality of the information extraction should be as high as possible, but, at the same time, the human effort should be low. Such contradicting goals are often an issue in system development; thus, our method proposed helped us to conceptualize and explore the design space. Based on a critical reflection on our method application, we want to offer a complementary perspective to the value-centered design of interactive intelligent systems. Our overarching goal is to contribute to the design of so-called hybrid systems where humans and computers are partners.

Wikidata promises to reduce factual inconsistencies across all Wikipedia language versions. It will enable dynamic data reuse and complex fact queries within the world's largest knowledge database. Studies of the existing participation patterns that emerge in Wikidata are only just beginning. What delineates most of the contributions in the system has not yet been investigated. Is it an inheritance from the Wikipedia peer-production system or the proximity of tasks in Wikidata that have been studied in collaborative ontology engineering? As a first step to answering this question, we performed a cluster analysis of participants' content editing activities. This allowed us to blend our results with typical roles found in peer-production and collaborative ontology engineering projects. Our results suggest very specialised contributions from a majority of users. Only a minority, which is the most active group, participate all over the project. These users are particularly responsible for developing the conceptual knowledge of Wikidata. We show the alignment of existing algorithmic participation patterns with these human patterns of participation. In summary, our results suggest that Wikidata rather supports peer-production activities caused by its current focus on data collection. We hope that our study informs future analyses and developments and, as a result, allows us to build better tools to support contributors in peer-production-based ontology engineering.

The emergence of the Social Web offers new opportunities for scientists to explore open virtual communities. Various approaches have appeared in terms of statistical evaluation, descriptive studies and network analyses, which pursue an enhanced understanding of existing mechanisms developing from the interplay of technical and social infrastructures. Unfortunately, at the moment, all these approaches are separate and no integrated approach exists. This gap is filled by our proposal of a concept which is composed of a universal description model, temporal network definitions, and a measurement system. The approach addresses the necessary interpretation of Social Web communities as dynamic systems. In addition to the explicated models, a software tool is briefly introduced employing the specified models. Furthermore, a scenario is used where an extract from the Wikipedia database shows the practical application of the software.

Researchers of computer science and social science are increasingly interested in the Social Web and its applications. To improve existing infrastructures, to evaluate the success of available services, and to build new virtual communities and their applications, an understanding of dynamics and evolution of inherent social and informational structures is essential. One key question is how communities which exist in these applications are structured in terms of author contributions. Are there similar contribution patterns in different applications? For example, does the so called onion model revealed from open source software communities apply to Social Web applications as well? In this study, author contributions in the open content project Wikipedia are investigated. Previous studies to evaluate author contributions mainly concentrate on editing activities. Extending this approach, the added significant content and investigation of which author groups contribute the majority of content in terms of activity and significance are considered. Furthermore, the social information space is described by a dynamic collaboration network and the topic coverage of authors is analyzed. In contrast to existing approaches, the position of an author in a social network is incorporated. Finally, a new composite calculation to evaluate author contributions in Wikis is proposed. The action, the content contribution, and the connectedness of an author are integrated into one equation in order to evaluate author activity.