Designing meaningful tangible and embodied interactions remains challenging due to their situated nature, complex user needs, and the limited programming skills of many users as well as designers. We developed an interaction model where users and LLMs co-perform tangible actions through prompt engineering beyond deterministic logic of commercial smart systems. In this model AI systems interpret natural language descriptions of environmental context, internalize technical functionalities and spatial cues, and translate these into tangible actions. We encapsulated the interaction model within a LLM-enabled tangible artifact as a HCI provotype and conducted an initial exploratory study around it. Our preliminary findings point to opportunities in refinement and reappropriation of such systems over the use period as well as challenges in adapting deictic spatial references.

The way we work is no longer hybrid—it is blended with AI co-workers, automated decisions, and virtual presence reshaping human roles, agency, and expertise. We now work through AI, with our outputs shaped by invisible algorithms. AI’s infiltration into knowledge, creative, and service work is not just about automation, but concerns redistribution of agency, creativity, and control. How do we deal with physical and distributed AI-mediated workspaces? What happens when algorithms co-author reports, and draft our creative work? In this provocation, we argue that hybrid work is obsolete. Blended work is the future, not just in physical and virtual spaces but in how human effort and AI output become inseparable. We argue this shift demands urgent attention to AI-mediated work practices, work-life boundaries, physical-digital interactions, and AI transparency and accountability. The question is not whether we accept it, but whether we actively shape it before it shapes us.

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.

Conversational interaction is becoming ubiquitous, offering new opportunities to make digital content more accessible and inclusive. While numerous guidelines exist in the literature to inform the design of conversational interfaces, these typically focus on "standalone"voice agents or chatbots. In this survey, we explore guidelines for conversational web browsing, a paradigm in which the conversational interface acts as a middleman between users and web pages. We survey the literature on conversational interaction and web accessibility to map design guidelines for accessible and inclusive voice interaction with web content.

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.

Remote collaboration technologies shape how workers are perceived by colleagues and managers, influencing career progression, trust, and workplace dynamics. This study examines visibility practices—also known as self-presentation or impression management—by exploring interactions through which remote workers establish and maintain visibility. Through 16 semi-structured interviews with remote knowledge workers across various roles and regions, we identify key visibility practices: participating in meetings, leaving traceable links to quality work outputs, and reappropriating miscellaneous features to become visible for others. However, these practices are deeply intertwined with negative psycho-social externalities such as internal pressures, fears, mistrust, and privacy concerns that endanger workers’ overall well-being. Our contributions include (1) empirical insights into workplace visibility and its entangled psycho-social complexities, (2) visibility ecosystem as a socio-material frame, capturing human-technology interactions in when visibility is at stake, and (3) design implications for collaboration technologies that support visibility practices while mitigating associated psycho-social externalities.

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.

The growing sophistication of Large Language Models allows conversational agents (CAs) to engage users in increasingly personalized and targeted conversations. While users may vary in their receptiveness to CA persuasion, stylistic elements and agent personalities can be adjusted on the fly. Combined with image generation models that create context-specific realistic visuals, CAs have the potential to influence user behavior and decision making. We investigate the effects of linguistic and visual elements used by CAs on user perception and decision making in a charitable donation context with an online experiment (n=344). We find that while CA attitude influenced trust, it did not affect donation behavior. Visual primes played no role in shaping trust, though their absence resulted in higher donations and situational empathy. Perceptions of competence and situational empathy were potential predictors of donation amounts. We discuss the complex interplay of user and CA characteristics and the fine line between benign behavior signaling and manipulation.

The European Commission proposed harmonised rules on artificial intelligence (AI) on the 21st of April 2021 (namely the EU AI Act). Following a consultative process with the European Council and many amendments, a General Approach of the EU AI Act was published on the 25th of November 2022. The EU Parliament approved the initial draft in May 2023. Trilogue meetings took place in June, July, September and October 2023, with the aim for the European Parliament, the Council of the European Union and the European Commission to adopt a final version early 2024. This is the first attempt to build a legally binding legal instrument on Artificial Intelligence in the European Union (EU). In a similar way as the General Data Protection Regulation (GDPR), the EU AI Act has an extraterritorial effect. It has, therefore, the potential to become a global gold standard for AI regulation. It may also contribute to developing a global consensus on AI Trustworthiness because AI providers must conduct conformity assessments for high-risk AI systems prior to entry into the EU market. As the AI Act contains limited guidelines on how to conduct conformity assessments and ex-post monitoring in practice, there is a need for consensus building on this topic. This paper aims at studying the governance structure proposed by the EU AI Act, as approved by the European Council in November 2022, and proposes tools to conduct conformity assessments of AI systems.

MobiTouch is a wearable haptic device that integrates pneumatic and vibrotactile technologies to enhance touch sensations. The system addresses the slow responsiveness of small pneumatic components by compensating with vibrations, allowing for more diverse touch patterns. The wrist prototype operates wireless, is powered by a rechargeable battery, and features customisable touch patterns through an external controller. MobiTouch’s design improves portability and responsiveness for haptic pneumatic wearables, offering potential applications in affective touch and wireless tactile transmission.

There is a call for more use of future-oriented design methods like speculative de-sign in developing policies. While these methods offer potential benefits in helping future-proof policies, they also run the risk of solidifying existing structures of pow-er if not applied critically. In this paper, we describe a case study examining smart doorbells in Amsterdam, where we created a speculative design exhibition grounded in feminist theory in order to challenge the existing power structures in the public domain. We then discuss the insights from our design process and the reaction the exhibition received in light of how feminist theory can help ensure a critical application of future-oriented design methods in policy design.

Empathy is considered a crucial construct within HCI and CSCW, yet our understanding of this complex concept remains fragmented and lacks consensus in existing research. In this scoping review of 121 articles from the ACM Digital Library, we synthesize the diverse perspectives on empathy and scrutinize its current conceptualization and operationalization. In particular, we examine the various interpretations and definitions of empathy, its applications, and the methodologies, findings, and trends in the field. Our analysis reveals a lack of consensus on the definitions and theoretical underpinnings of empathy, with interpretations ranging from understanding the experiences of others to an affective response to the other's situation. We observed that despite the variety of methods used to gauge empathy, the predominant approach remains self-assessed instruments, highlighting the lack of novel and rigorously established and validated measures and methods to capture the multifaceted manifestations of empathy. Furthermore, our analysis shows that previous studies have used a variety of approaches to elicit empathy, such as experiential methods and situational awareness. These approaches have demonstrated that shared stressful experiences promote community support and relief, while situational awareness promotes empathy through increased helping behavior. Finally, we discuss a) the potential and drawbacks of leveraging empathy to shape interactions and guide design practices, b) the need to find a balance between the collective focus of empathy and the (existing and dominant) focus on the individual, and c) the careful testing of empathic designs and technologies with real-world applications.

The ‘gender mathematics gap’ which persists in many countries means that women students may, on average, have less high school preparation in mathematics than men students entering engineering education. This in turn could impact their performance in first-year exams and thus reduce women’s participation in engineering programs. One factor that has been a focus of some interest in addressing equity issues in education is time-limited exams, which have been found to give rise to unfairness with respect to underrepresented students in a number of domains. In mathematics, time pressure has been found to be linked to increased student stress and to the use of less effective problem-solving strategies in assessment conditions. We sought to explore, therefore, the impact of reducing time pressure in a first-year engineering Linear Algebra course. We had 275 participants, of which 192 (69.8%) were men and 83 (30.2%) were women. Using a pseudo-experimental design in real-word conditions which controlled for teacher effects and assessment effects, we found that, when there was reduced time pressure, students with less prior mathematics performed better than when in a more time-pressured exam. Our results show that these students can learn the required Linear Algebra and can demonstrate their learning under appropriate conditions. This leads us to conclude that reducing time pressure in first year mathematics exams may contribute to improving the retention of women students in engineering education, particularly in cultural contexts in which a gender mathematics gap is prevalent.

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.

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.

Large Language Models (LLMs) are expected to significantly impact various socio-technical systems, offering transformative possibilities for improved interaction between humans and technology. However, their integration poses complex challenges due to the intricate interplay between societal structures, human behaviour, and technological innovation. This research explores these multifaceted challenges, emphasising the need for a human-centered approach in integrating LLMs to ensure that technological advancements are aligned with ethical standards and societal needs. Utilizing a structured methodology comprising a workshop, literature analysis, and expert collaborations, the study uses a multi-dimensional human-centered AI framework to guide the responsible integration of LLMs. Key insights include the importance of inclusive data, considering unintended consequences, maintaining privacy, and respecting intellectual property rights. The paper identifies and advocates for principles like human-in-the-loop, continuous longitudinal studies, proactive awareness campaigns, and regular audits to develop LLMs that are ethically sound, adaptable, and effectively integrated into various socio-technical systems, thus addressing user needs and broader societal impacts. The paper also underlines the importance of collaboration among academia, industry, and policymakers to develop LLMs that are ethically aligned, socially beneficial, and adaptable to future societal needs. The findings offer valuable insights into the strategic integration of LLMs, advocating for a broader research perspective beyond industrial motivations to fully understand and leverage LLMs in socio-technical landscapes.

The EmpathiCH Workshop aims to blend a diverse set of expertise to expand upon the nascent field of Empathy-Centric Design. Building on the discussions in previous editions of the workshop, this iteration invites contributions which scrutinize the use of empathy as a design principle in digital interfaces. We encourage inquiry in a number of research dimensions: examining the multifaceted nature of empathy; establishing both the requirements and shortcomings of empathy in design research; discussing key post-human stakeholders in digital interfaces (social groups, causes, digital avatars, artificial agents etc.); and expanding the scope of empathy research beyond preliminary perspective-taking. The workshop, structured as a combination of author panels, expert discussion, and interactive activities, provides the ideal venue to foster a critical discussion on the nature of the suitability of empathy in digital design, especially in the rapidly approaching context of its role in post-humanist HCI.

Background: Radiomics, the field of image-based computational medical biomarker research, has experienced rapid growth over the past decade due to its potential to revolutionize the development of personalized decision support models. However, despite its research momentum and important advances toward methodological standardization, the translation of radiomics prediction models into clinical practice only progresses slowly. The lack of physicians leading the development of radiomics models and insufficient integration of radiomics tools in the clinical workflow contributes to this slow uptake. Methods: We propose a physician-centered vision of radiomics research and derive minimal functional requirements for radiomics research software to support this vision. Free-to-access radiomics tools and frameworks were reviewed to identify best practices and reveal the shortcomings of existing software solutions to optimally support physician-driven radiomics research in a clinical environment. Results: Support for user-friendly development and evaluation of radiomics prediction models via machine learning was found to be missing in most tools. QuantImage v2 (QI2) was designed and implemented to address these shortcomings. QI2 relies on well-established existing tools and open-source libraries to realize and concretely demonstrate the potential of a one-stop tool for physician-driven radiomics research. It provides web-based access to cohort management, feature extraction, and visualization and supports “no-code” development and evaluation of machine learning models against patient-specific outcome data. Conclusions: QI2 fills a gap in the radiomics software landscape by enabling “no-code” radiomics research, including model validation, in a clinical environment. Further information about QI2, a public instance of the system, and its source code is available at https://medgift.github.io/quantimage-v2-info/. Key points As domain experts, physicians play a key role in the development of radiomics models.Existing software solutions do not support physician-driven research optimally.QuantImage v2 implements a physician-centered vision for radiomics research.QuantImage v2 is a web-based, “no-code” radiomics research platform.

The digitization of services and global lock-downs have led an explosion of delivery services, which use gig-workers as delivery personnel. They can face apathy from both their employers and users of the service. Previous studies focused on mediating interactions between workers or workers and tasks. However, delivery presents the opportunity for HCI interventions to mediate the interaction between worker and users to increase their empathy. We conducted an empirical study where 63 participants ordered a drink with an app which presented a different level of information about the delivery person (nothing; name and photo; heart rate). Initial results show no significant impact on empathy measures between conditions, however post-hoc analysis showed that heart rate lead to increased Compassionate and decreased Affective empathy. This raises the question of what "type"of empathy is beneficial for delivery personnel and the need to refine the concept and measures of empathy used in HCI.