With psychiatry lagging behind other medical fields in terms of innovation in instruments and methods, AI provides it an opportunity to catch up. Advocates of digital phenotyping promise to provide an objective tool that detects symptoms by analysing data from personal devices. We argue that digital phenotyping requires a more reflexive and critical approach to its design and an alignment of the clinicians’ interests in generating relevant evidence with the needs of service users who seek tools to manage their condition. We propose a felt informatics approach, situating digital phenotyping design within the problem space of pragmatist aesthetics. Within this perspective, felt life becomes a central object and a site for digital phenotyping design. This paper reveals the ways diagnostic data mediates mental ill health experience, emphasises the cultivation of aesthetic sensibility as a fundamental element of digital phenotyping and includes design considerations for practitioners and researchers.

In organizations, the interest in automation is long-standing. However, adopting automated processes remains challenging, even in environments that appear highly standardized and technically suitable for it. Through a case study in Amsterdam Airport Schiphol, this paper investigates automation as a broader sociotechnical system influenced by a complex network of actors and contextual factors. We study practitioners' collective understandings of automation and subsequent efforts taken to implement it. Using imaginaries as a lens, we report findings from a qualitative interview study with 16 practitioners involved in airside automation projects. Our findings illustrate the organizational dynamics and complexities surrounding automation adoption, as reflected in the captured problem formulations, conceptions of the technology, envisioned human roles in autonomous operations, and perspectives on automation fit in the airside ecosystem. Ultimately, we advocate for contextual automation design, which carefully considers human roles, accounts for existing organizational politics, and avoids techno-solutionist approaches.

Contestability has been proposed as a key element in designing algorithmic decision-making processes that safeguard decision subjects' rights to dignity and autonomy. However, little is known about how contestability can be operationalized based on decision subjects' needs and preferences. We address this research gap by identifying decision subjects' information and procedural needs for enacting meaningful contestability. To this end, we chose an illegal holiday rental detection scenario as our case; a high-risk decision-making process in the public sector. We conducted 21 semi-structured interviews with citizens with experience renting their homes out and different levels of AI literacy. We found that decision subjects request interventions that facilitate (1) cooperation in sense-making, (2) support in contestation acts, and (3) appropriate responsibility attribution. Our results highlight the cooperative work behind contestability, and motivate future efforts to structure individual and collective action, to personalize explanations for contestability, and to open up sites of contestation in AI pipelines.

ChatGPT and other large language models (LLMs) have proven useful in crowdsourcing tasks, where they can effectively annotate machine learning training data. However, this means that they also have the potential for misuse, specifically to automatically answer surveys. LLMs can potentially circumvent quality assurance measures, thereby threatening the integrity of methodologies that rely on crowdsourcing surveys. In this paper, we propose a mechanism to detect LLM-generated responses to surveys. The mechanism uses ''prompt injection,'' such as directions that can mislead LLMs into giving predictable responses. We evaluate our technique against a range of question scenarios, types, and positions, and find that it can reliably detect LLM-generated responses with more than 98% effectiveness. We also provide an open-source software to help survey designers use our technique to detect LLM responses. Our work is a step in ensuring that survey methodologies remain rigorous vis-a-vis LLMs.

This paper innovates in the relationship between sustainable technology suppliers and users, using the example of heat pumps. Heat pumps are necessary for energy transitions in Europe. However, in everyday life in households, heat pumps are often not used as the technology developers intended. This discrepancy presents a challenge for heat pump supply-side actors such as manufacturers and resellers. This paper first presents a design perspective on user improvisation and highlights its value for innovation. We synthesized the perspective in a sensitizing video. We then employed this video to engage with nine supply-side professionals in the Dutch heat pump value network and conducted semi-structured interviews with them to understand their responses to improvisation. We categorized their responses and identified the factors influencing the choice of response. We identify ten different responses and nine motivating factors. We then interpret the responses in the light of our design perspective on user improvisation to highlight areas for socio-technical innovation in the relationship between the heat pump supply and use sides. This innovation can support heat pump uptake and satisfaction and thus improve the quality and rate of renovations.

Human intervention is claimed to safeguard decision-subjects’ rights in algorithmic decision-making and contribute to their fairness perceptions. However, how decision-subjects perceive hybrid decision-maker configurations (i.e., combining humans and algorithms) is unclear. We address this gap through a mixed-methods study in an algorithmic policy enforcement context. Through qualitative interviews (Study 1; N1 = 21), we identify three characteristics (i.e., decision-maker’s profile, model type, input data provenance) that affect how decision-subjects perceive decision-makers’ ability, benevolence, and integrity (ABI). Through a quantitative study (Study 2; N2 = 223), we then systematically evaluate the individual and combined effects of these characteristics on decision-subjects’ perceptions towards decision-makers, and fairness perceptions. We found that only decision-maker’s profile contributes to perceived ability, benevolence, and integrity. Interestingly, the effect of decision-maker’s profile on fairness perceptions was mediated by perceived ability and integrity. Our findings have design implications for ensuring effective human intervention as a protection against harmful algorithmic decisions.

This paper presents a conceptual exploration of designing sexual pleasure as an evolving whole-body experience. It addresses the historically narrow focus of research and technology on functional outcomes such as reproduction and orgasm. This limited perspective overlooks diverse desires, emotional connection, and sensory engagement, reinforcing restrictive norms that shape how individuals conceptualise and experience sexuality. To inform our design inquiry, we conducted a qualitative survey (N=143) to generate how individuals understand and experience sexual pleasure. Reflexive thematic analysis of the responses reveals the influence of culture and technology on sexuality, alongside several experiential dimensions: emotional and embodied connection, play and sensory immersion, and vulnerability. These insights, together with a theoretical foundation, guide a design exploration communicated through two provocations. These provocations serve as reflections of an alternative design orientation; one that challenges normative assumptions, views pleasure as an ongoing process, supports bodily exploration, and facilitates richer, more inclusive sexual experiences.

Cancer treatments often lead to sexual health challenges that greatly impact cancer survivors’ quality of life. Current interventions primarily address physiological aspects, like medication or vaginal care, overlooking psychological, social, and cultural dimensions. This paper explores how HCI can address this gap by supporting post-cancer sexual health with interventions for survivors and their partners, considering their lived experiences. Through reflexive thematic analysis of interviews with (N=6) medical sexologists, we identified five themes: perceiving the body as a medical object, the hot potato problem in oncology, sociotechnical sexploration, reuniting what treatment has divided, and designing interventions with openness in a highly situated context. These themes highlight cancer survivors’ experiences, the (in)effectiveness of current interventions, and provision of care. This research outlines the design space for post-cancer sexual health by providing specific design directions (“what”) and ways for designing them (“how”), while advancing the broader discourse on intimacy and design within HCI.

Access to greenspace impacts children’s physical, social, and mental health. Numerous factors affect children’s access to urban greenspaces, often distinct from those affecting the general population, including parental restrictions, limited routine activity-space, and particular preferences. Most accessibility measures, however, employ the same principles for children and the general population, and a comprehensive exploration of factors and corresponding measures remains lacking. We conduct a scoping review and workshops with researchers and practitioners to identify factors affecting children’s access to greenspace, synthesize them into a conceptual model, and assess how existing accessibility measures address these factors. We focus on children aged 6–11 years old. Our analysis indicates children’s access involves a trade-off between reachability, determined by the route connecting the child’s starting setting to greenspace, and attractiveness, determined by how the greenspaces adhere to the child’s, and their companions’, preferences and motivations for visiting. Safety perceptions are important throughout. Existing accessibility measures predominantly emphasize reachability, neglecting personal characteristics and motivations. Based on our findings, we propose future directions for developing child-centered accessibility metrics. Our overview of metrics can facilitate decision-making in the selection of suitable measures, while our conceptual model can foster shared understanding of factors affecting children’s access to urban greenspace.

Sustained adoption of automation is a problem for organizations, despite the promised benefits of automation and the propensity for organizations to expect it to transform their workplaces. To address this problem, previous work in HCI has mostly considered the perspectives and experiences of users interacting with automation technologies and has not considered the broader organizational context consisting of different stakeholders with varying needs and expectations around automation. Taking Amsterdam Airport Schiphol as a case study, we examine the challenges faced by practitioners responsible for integrating automation projects in its airside ecosystem, in an interview study conducted with 8 participants. Our findings reveal three challenges to the adoption of automation within the organization - the lack of consensus among different stakeholders, the need to adapt the technology to the specific context, and the undefined procedures required to maintain automation after implementation, that should be further addressed by the HCI community.

The environmental pressure, CO2 emissions (including embodied energy) and delivery risks of our digital infrastructures are increasing. The exponentially growing digitisation of services that drive the transition from industry 4.0 to industry 5.0 has resulted in a rising materials demand for ICT hardware manufacturing. ICT devices such as laptops and data servers are being used on average for 3 and 4–5 years respectively (van Driel (2020)), while research shows that they should last 7 years before replacement (Journal of Cleaner Production69 (2014), 10–16). A solution is to transition from a linear to a circular economy (CE), through which materials that were previously disposed of as waste are re-entered back into product life-cycles through processes such as reuse, recycling, remanufacturing, repurposing. However, the adoption of the CE in the ICT sector is currently limited due to the lack of tools that support knowledge exchange between sustainability, ICT and technology experts in a standardised manner and the limited data availability, accessibility and interoperability needed to build such tools. Further, the already existing knowledge of the domain is fragmented into silos and the lack of a common terminology restricts its interoperability and usability. These also lead to transparency and responsibility issues along the supply chain. For many years now, the Semantic Web has been known to provide solutions to such issues in the form of ontologies. Several ontologies for the ICT, materials and CE domains have been build and successfully utilised to support processes such as predictive maintenance. However, there is a lack of a systematic analysis of the existing ontologies in these domains. Motivated by this, we present a literature survey and analysis of, but not limited to, existing ontologies for ICT devices such as laptops, materials and the CE. In addition, we discuss the need for findable, accessible, interoperable, reusable (FAIR) data in the CE, different factors such as data privacy and security that affect this and the role of ontologies.

In the shift towards human-centered manufacturing, our two-year longitudinal study investigates the real-world impact of deploying Cognitive Assistants (CAs) in factories. The CAs were designed to facilitate knowledge sharing among factory operators. Our investigation focused on smartphone-based voice assistants and LLM-powered chatbots, examining their usability and utility in a real-world factory setting. Based on the qualitative feedback we collected during the deployments of CAs at the factories, we conducted a thematic analysis to investigate the perceptions, challenges, and overall impact on workflow and knowledge sharing.

Our results indicate that while CAs have the potential to significantly improve efficiency through knowledge sharing and quicker resolution of production issues, they also introduce concerns around workplace surveillance, the types of knowledge that can be shared, and shortcomings compared to human-to-human knowledge sharing. Additionally, our findings stress the importance of addressing privacy, knowledge contribution burdens, and tensions between factory operators and their managers.

Digitally-supported participatory methods are often used in policy-making to develop inclusive policies by collecting and integrating citizen's opinions. However, these methods fail to capture the complexity and nuances in citizen's needs, i.e., citizens are generally unaware of other's needs, perspectives, and experiences. Consequently, policies developed with this underlying gap tend to overlook the alignment of multistakeholder perspectives, and design policies based on the optimization of high-level demographic features. In our contribution, we propose a method to enable citizens understand other's perspectives and calibrate their positions. First, we collected requirements and design principles to develop our approach by involving stakeholders and experts in policymaking in a series of workshops. Then, we conducted a crowdsourcing study with 420 participants to compare the effect of different text and images, on people's initial and final motivations and their willingness to change opinions. We observed that both influence participant's opinion change, however, the effect is more pronounced for textual modality. Finally, we discuss overarching implications of designing with empathy to mediate alignment of citizen's perspectives.

Accessibility is a widely used concept across various disciplines to evaluate the degree to which individuals can reach desired destinations. Conventionally, accessibility is determined by the attractiveness of a destination and the associated travel cost to reach it. However, existing place-based accessibility measures do not differentiate between destinations accessible to individuals from a single demographic group and those accessible to individuals from diverse demographic groups. We propose a measure to assess the potential of distinct destinations to bring different individuals and demographic groups together, defining this property as co-accessibility. We demonstrate how measuring co-accessibility can enhance existing accessibility measures, describe its components, and provide a mathematical formulation for quantifying it. To illustrate the practical application of our measure, we conduct a case study in Amsterdam, the Netherlands, comparing the accessibility and co- accessibility of various destinations. This sample case study highlights the complexities and challenges inherent in measuring co-accessibility. Building on existing literature and our analysis results, we discuss the potential implications of co-accessibility, identify key challenges in its assessment, and recommend directions for future research.

Machine learning (ML) training data is often scattered across disparate collections of datasets, called <italic>data silos</italic>. This fragmentation poses a major challenge for data-intensive ML applications: integrating and transforming data residing in different sources demand a lot of manual work and computational resources. With data privacy constraints, data often cannot leave the premises of data silos; hence model training should proceed in a decentralized manner. In this work, we present a vision of bridging traditional data integration (DI) techniques with the requirements of modern machine learning systems. We explore the possibilities of utilizing metadata obtained from data integration processes for improving the effectiveness, efficiency, and privacy of ML models. Towards this direction, we analyze ML training and inference over data silos. Bringing data integration and machine learning together, we highlight new research opportunities from the aspects of systems, representations, factorized learning, and federated learning.

Pre-trained deep learning (DL) models are increasingly accessible in public repositories, i.e., model zoos. Given a new prediction task, finding the best model to fine-tune can be computationally intensive and costly, especially when the number of pre-trained models is large. Selecting the right pre-trained models is crucial, yet complicated by the diversity of models from various model families (like ResNet, Vit, Swin) and the hidden relationships between models and datasets. Existing methods, which utilize basic information from models and datasets to compute scores indicating model performance on target datasets, overlook the intrinsic relationships, limiting their effectiveness in model selection. In this study, we introduce TransferGraph, a novel framework that reformulates model selection as a graph learning problem. TransferGraph constructs a graph using extensive metadata extracted from models and datasets, while capturing their inherent relationships. Through comprehensive experiments across 16 real datasets, both images and texts, we demonstrate TransferGraph's effectiveness in capturing essential model-dataset relationships, yielding up to a 32% improvement in correlation between predicted performance and the actual fine-tuning results compared to the state-of-the-art methods.

Domestic heating systems need to change to meet climate targets. We draw on practice theoretical concepts to understand what is needed to integrate heat pumps in Dutch households. From a design orientation, we view households as creative actors integrating technologies into daily life. We report on an ethnographic study of the disruptions and resulting reconfigurations that occur when heat pumps are introduced in Dutch households. Our findings reveal a variety of practice reconfigurations around heat pumps. We also find that these reconfigurations are related to and may influence other practices, including professional practices. We discuss our findings in relation to policy, technology development, and design, and conclude that the required reconfigurations in Dutch household practices could be supported, and that innovative practice reconfigurations emerging from internal household dynamics could contribute to sustainability transitions.

The study of urban greenspaces typically relies on three types of data: people’s subjective perceptions collected via questionnaires, vegetation indices derived from satellite imagery, such as the Normalized Difference Vegetation Index (NDVI), and Land Use or Land Cover maps, such as OpenStreetMap (OSM). Data on people’s perceptions are essential when researching human activities, yet they scale poorly. NDVI and OSM data, on the other hand, are freely available worldwide, thus valuable for assessing cities at scale or prioritizing locations for interventions. However, it is unclear how effectively NDVI and OSM data capture people’s visual perceptions of urban greenspaces. In this work, we collect people’s visual perceptions of public spaces in three major European cities through crowdsourcing, quantitatively compare them to NDVI and OSM data, and qualitatively investigate disparities. We found that NDVI moderately correlates with perceived greenness and that not only OSM greenspaces but also pocket parks and play spaces are often considered green. Furthermore, we found that people’s perceptions correspond best to OSM data in small radius distances and NDVI data in larger radius distances and that combining NDVI and OSM data can improve identification of places in OSM that are commonly considered green. Our qualitative analysis revealed that configuration and variety of vegetation, and presence of other natural or built-up features, influence people’s perceptions of greenspace. With our findings we aim to help researchers and practitioners make more informed decisions when collecting greenspace data for their specific context, ultimately contributing to green urban environments that reflect people’s perspectives.

The configuration of public open spaces plays a crucial role in shaping how different people use them. Nevertheless, our understanding of how the physical features of public open spaces influence the activities conducted within them, and the extent to which this impact differs across various individuals and population groups, is currently limited. In this study, we explore how the physical characteristics of public open spaces influence the likelihood of use among individuals, spanning different age and gender groups. By employing crowdsourcing, street-level imagery, statistical comparisons, and reflexive thematic analysis we uncover significant variations in the suitability of public open spaces for distinct activities, such as socializing or exercising. Greenspaces emerge as the preferred choice for almost all activities, whereas streets are consistently rated as the least suitable. Additionally, we identified various characteristics that influence the activities people are likely to engage in. These include the size of the space, the presence of seating, natural elements such as vegetation or water bodies, and the proximity to transport infrastructure. Surprisingly, we do not observe statistically significant differences in preferences among most age and gender groups. Overall, our study underscores the need for providing a diverse range of public open spaces tailored to accommodate different individuals, population groups, and activities.