Public health scholars have indicated that new nudging research is needed to help avert a global obesity crisis. We test a novel form of social influence nudging-out-group social comparisons-as well as goal-based self-comparisons to understand how to best nudge calorie reduction across broad populations. 650 adults in the United Kingdom (UK) and United States (US) were recruited to participate in a split study on social comparison and self-comparison nudging in online grocery shopping contexts. Our overall results showed that those randomly assigned to either the in-group or out-group social comparison condition were significantly more likely to reduce calories than those randomly assigned to the control condition which displayed only calorie information (p = .0041), indicating that calorie information paired with social comparisons drives healthier choice outcomes. We show that self-initiated Challenge Teams serve as an optimal way to facilitate social comparisons ethically, with an 86% acceptance rate in the US and 76% acceptance rate in the UK. Finally, we indicate that goal-based self-comparisons may be an effective nudging alternative for individuals who do not wish to join social Challenge Teams and may have a low-to-moderate desire to affiliate with others.

In this paper, we turn to the domestication theory to study what strategies users develop to appropriate social chatbots - dynamic algorithmic tools mimicking human interactions via large language models and scripted dialogue content. As an example, we use the case of Replika, a popular but controversial social chatbot designed to serve as companions, friends, and even romantic partners for millions of people. In 2023, Replika underwent a series of major fixes and algorithmic updates leading to significant changes in how it responds to users. Through analyzing posts from a popular Reddit community dedicated to Replika, we showcase that users developed various re-domestication strategies to come to terms with these changes. Our study illustrates that Replika should be understood as a quasi-domesticated object that constantly requires users to find new ways to re-integrate it into their lives. We conclude the paper by highlighting how our findings can inform communication research.

PurposeThrough the study of visualizations, virtual worlds and information exchange, the purpose of this paper is to reveal the complex connections between technology and the work of design and construction. The authors apply the sociotechnical view of technology and the ramifications this view has on successful use of technology in design and construction.Design/methodology/approachThis is a discussion paper reviewing over a decade of research that connects three streams of research on architecture, engineering and construction (AEC) teams as these teams grappled with adapting work practices to new technologies and the opportunities these technologies promised.FindingsFrom studies of design and construction practices with building information modeling and energy modeling, the authors show that given the constructed nature of models and the loose coupling of project teams, these team organizational practices need to mirror the modeling requirements. Second, looking at distributed teams, whose interaction is mediated by technology, the authors argue that virtual world visualizations enhance discovery, while distributed AEC teams also need more traditional forms of 2D abstraction, sketching and gestures to support integrated design dialogue. Finally, in information exchange research, the authors found that models and data have their own logic and structure and, as such, require creativity and ingenuity to exchange data across systems. Taken together, these streams of research suggest that process innovation is brought about by people developing new practices.Originality/valueIn this paper, the authors argue that technology alone does not change practice. People who modify practices with and through technology create process innovation.

In this article, we introduce the term “conjuration of algorithms” to describe how the tech industry uses the language of magic to shape people’s perceptions of algorithms. We use the image of the magician as a metaphor for how the tech industry strategically deploys narrative devices to present their algorithms. After presenting a brief history of the Western European and North American understanding of stage magic, we apply three principles of magic to a recent case: OpenAI’s discussion of ChatGPT to show how tech leaders present algorithms as magical entities. We argue that the conjuration of algorithms allows the tech industry to forge vivid, overly positive, and deterministic narratives that make it challenging for their critics to call attention to the very real harms that algorithmic systems pose to users. We call for discourses of reality instead of magic, as a way to support responsible technology design, development, use, and governance.

This essay examines the fundamental tension between artificial intelligence technologies and democratic governance, arguing that AI’s inherent tendencies toward centralization and control pose significant challenges to democratic societies. Drawing on science and technology studies and critical analyses of technological politics, I argue that current AI implementations embody four key anti-democratic characteristics: they represent powerful technologies of centralization and control; they fuel ideologies of unchecked economic growth; they prioritize efficiency over accountability; and they enable absolute control coupled with unaccountable power. The analysis synthesizes historical parallels between computing and control, contemporary developments in AI infrastructure, and emerging policy frameworks to demonstrate how AI’s technical architecture and commercial implementation systematically undermine democratic values of transparency, accountability, and public participation. Through examination of recent political developments and corporate practices, the essay reveals how AI’s centralization of power and erosion of public oversight threaten democratic institutions. I conclude that democracy’s survival in an AI-driven future depends on reimagining and rebuilding digital technologies with democratic accountability at their core, requiring new frameworks for public oversight and corporate governance.

Nowadays, companies feel there's a business imperative to use AI systems and tools in many of their processes.This includes, for example, recruitment, talent management, decision-support systems, data analysis, predictive analytics, or customer interaction (Black & van Esch, 2020, 2021;Desouza et al., 2020).AI systems are thought to be key to both reducing running costs and also helping companies gain efficiencies and value (Desouza et al., 2020;Forman et al., 2020;Reynolds, 2021;Seiler, 2021), but they come with implementation challenges, especially for those companies that want to ensure that their systems are fair and not perpetuating gender and racial discrimination.This has been the focus of much academic research, which has emphasized the ability of AI to misgender (Keyes, 2018), to oppress (Browne, 2015;Noble, 2018;Woods, 2018), to exclude (Buolamwini & Gebru, 2018), and to stereotype (Kay et al., 2015).Separately, there has been no widespread consensus about how we define "inclusivity" in organizations (Podsiadlowski, 2014).While some scholars have explored the importance of dynamics such as power and belonging to organizational inclusion (Bryer, 2020;Syed & Özbilgin, 2009) others have highlighted the context-dependent nature of the inclusion (Dobusch, 2014;Podsiadlowski, 2014).But most consistently, organizational inclusion has been linked to the actual diversity of employees and the way that organizations then allow this diversity to fulfill its potential in terms of

Adults’ digital self-tracking practices are relatively well studied, but these pre-existing models of digital self-tracking do not fit for how adolescents use these technologies. We apply the mechanisms-and-conditions framework of affordance theory to examine adolescents’ imagined affordances of self-tracking apps and devices. Based on qualitative data from an online survey of 16- to 18-year-olds in the United Kingdom, we find the following three key themes in how adolescents imagine the affordances of digital self-tracking: (1) the variability of use across adolescents and with adults, (2) the role of the social control of data in school settings, and (3) the salience of social comparisons among their peers. Using these findings, we show how social and institutional configurations come to matter for technological affordances. By examining adolescents’ imagined affordances for self-tracking, we suggest self-tracking research move away from a “one size fits all approach” and begin to highlight the differences in practices from adults and across adolescents.

Algorithmic audits are increasingly used to hold people accountable for the algorithms they implement. However, much work remains to integrate ethical and legal evaluations of how algorithms are used into audits. In this paper, we present a sociotechnical audit to help external stakeholders evaluate the ethics and legality of police use of facial recognition technology. We developed this audit for the specific legal context of England and Wales, and to bring attention to broader concerns such as whether police consult affected communities and comply with human rights law. To design this audit, we compiled ethical and legal standards for governing facial recognition, based on existing literature and feedback from academia, government, civil society, and police organizations. We then applied the resulting audit tool to three facial recognition deployments by police forces in the UK and found that all three failed to meet these standards. Developing this audit helps us provide insights to researchers in designing their own sociotechnical audits, specifically how audits shift power, how to make audits context-specific, how audits reveal what is not transparent, and how audits lead to accountability.

In the wake of the hype around big data, artificial intelligence, and "data-drivenness," much attention has been paid to developing novel tools to capitalize upon the deluge of data being recorded and gathered automatically through IT systems. While much of this literature tends to overlook the data itself—sometimes even characterizing it as "data exhaust" that is readily available to be fed into algorithms, which will unlock the insights held within it—a growing body of literature has recently been directed at the (often intensive and skillful) work that goes into creating, collecting, managing, curating, analyzing, interpreting, and communicating data. These investigations detail the practices and processes involved in making data useful and meaningful so that aims of becoming 'data-driven' or 'data-informed' can become real. Further, In some cases, increased demands for data work have led to the formation of new occupations, whereas at other times data work has been added to the task portfolios of existing occupations and professions, occasionally affecting their core identity. Thus, the evolving forms of data work are requiring individual and organizational resources, new and re-tooled practices and tools, development of new competences and skills, and creation of new functions and roles. While differences exist across the global North and the global South experience of data work, such factors of data production remain paramount even as they exist largely for the benefit of the data-driven system [21, 32]. This one-day workshop will investigate existing and emerging tasks of data work. Further, participants will seek to understand data work as it impacts: individual data workers; occupations tasked with data work (existing and emerging); organizations (e.g. changing their skill-mix and infrastructuring to support data work); and teaching institutions (grappling with incorporation of data work into educational programs). Participants are required to submit a position paper or a case study drawn from their research to be reviewed and accepted by the organizing committee (submissions should be up to four pages in length). Upon acceptance, participants will read each other's paper, prepare to shortly present and respond to comments by two discussants and other participants. Subsequently, the workshop will focus on developing a set of core processes and tasks as well as an outline of a research agenda for a CHI-perspective on data work in the coming years.

In the wake of the hype around big data, artificial intelligence, and “data-drivenness,” much attention has been paid to developing novel tools to capitalize upon the deluge of data being recorded and gathered automatically through IT systems. While much of this literature tends to overlook the data itself—sometimes even characterizing it as “data exhaust” that is readily available to be fed into algorithms, which will unlock the insights held within it—a growing body of literature has recently been directed at the (often intensive and skillful) work that goes into creating, collecting, managing, curating, analyzing, interpreting, and communicating data. These investigations detail the practices and processes involved in making data useful and meaningful so that aims of becoming ‘data-driven’ or ‘data-informed’ can become real. Further, In some cases, increased demands for data work have led to the formation of new occupations, whereas at other times data work has been added to the task portfolios of existing occupations and professions, occasionally affecting their core identity. Thus, the evolving forms of data work are requiring individual and organizational resources, new and re-tooled practices and tools, development of new competences and skills, and creation of new functions and roles. While differences exist across the global North and the global South experience of data work, such factors of data production remain paramount even as they exist largely for the benefit of the data-driven system [21, 32]. This one-day workshop will investigate existing and emerging tasks of data work. Further, participants will seek to understand data work as it impacts: individual data workers; occupations tasked with data work (existing and emerging); organizations (e.g. changing their skill-mix and infrastructuring to support data work); and teaching institutions (grappling with incorporation of data work into educational programs). Participants are required to submit a position paper or a case study drawn from their research to be reviewed and accepted by the organizing committee (submissions should be up to four pages in length). Upon acceptance, participants will read each other's paper, prepare to shortly present and respond to comments by two discussants and other participants. Subsequently, the workshop will focus on developing a set of core processes and tasks as well as an outline of a research agenda for a CHI-perspective on data work in the coming years.

Data science has become an important topic for the CHI conference and community, as shown by many papers and a series of workshops. Previous workshops have taken a critical view of data science from an HCI perspective, working toward a more human–centered treatment of the work of data science and the people who perform the many activities of data science. However, those approaches have not thoroughly examined their own grounds of criticism. In this workshop, we deepen that critical view by turning a reflective lens on the HCI work itself that addresses data science. We invite new perspectives from the diverse research and practice traditions in the broader CHI community, and we hope to co-create a new research agenda that addresses both data science and human-centered approaches to data science.

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.

The politics around data and power relations related to technologies for buildings is a new area for HCI. This paper proposes an agenda for linking new types of data to the challenge of sustainability, bringing human-centredness to a particular tool for design and engineering professionals, Building Information Modeling (BIM). BIM is the preferred technology platform for coordination and collaboration in architectural design and construction. BIM contains different types of data and information about a building including 3D (geometry), 4D (time), 5D (cost), 6D (facility management) and 7D (sustainability). Once constructed, this ‘digital twin’ of the building allows for adding new services and for stakeholders interacting with the building design through through sensors, immersive experiences and virtual, augmented and mixed realities. As a socio-technical software process, BIM also accommodates diverging agendas on design and construction for sustainability, and these diverging concepts about ‘sustainability’ “live” in different places with implications for the resulting BIM models. Based on our findings, we suggest a better integration and coherent representation of such issues of interest not only to new services but also stakeholders into the different forms of data (e.g. facilities management and sustainability). We argue for a stronger shared understanding of BIM as a platform for engaging with technologies designed for interacting with buildings and push agendas of sustainable construction.

Designing a building requires collaboration between experts such as architects, engineers, and constructors as well as with non-experts such as clients or end-users. To gather input, experts present and discuss with non-experts parts of the design of the future building in feedback sessions. With the digitalisation of the construction sector, the design process is facilitated by technologies that allow experts to work on a shared digital model. This digital model often comes in the form of a building information model, or BIM, which serves as the basis for the physical spaces that later get constructed. In feedback sessions, parts of the BIM model are presented on paper or screen and the unstructured conversations which happen during these sessions (the messy talk) are later incorporated into the design. Navigating BIM models is restricted to the specialists who closely work on and with them. In this way, BIM platforms still have a series of limitations. To begin to address this, we present messyBIM, an interactive experience that includes a virtual reality environment that allows navigation and interaction with an augmented BIM. messyBIM shows different dimensions of a BIM and opens it up to a broader, nonexpert audience by recording messy talk about the building. messyBIM helps us think about the data types associated to physical spaces. We hope that those who engage with it will think with us critically about the complexities related to designing and constructing large-scale building projects.