Access to cervical cancer care remains limited in sub-Saharan Africa, where women face compounded socio-cultural, gendered, and structural barriers. This qualitative study explores the lived experiences of nine women diagnosed with cervical cancer in Ethiopia and develops an empirically grounded patient journey map as a design and process artifact based on semi-structured interviews. The journey map reveals fragmented, non-linear care pathways, showing how barriers accumulate from symptom recognition through diagnosis, treatment, and post-treatment support. By visualizing breakdowns and transitions across the care path, the artifact supports problem framing, reflection, and identification of design opportunities. These disruptions intensify emotional and practical burdens, highlighting critical gaps in health literacy, information access, and continuity of care in the healthcare structure. The journey map defines a design-relevant problem space for context-sensitive digital health interventions. This work provides evidence for HCI researchers and practitioners to address accessibility barriers in cervical cancer care in low-resource settings.

Over 1.3 billion people worldwide live with long-term disabilities, yet many still face systemic exclusion despite advances in accessibility policy and technology. New regulations such as the EU Accessibility Act demand comprehensive transitions, but compliance risks becoming a superficial “checklist” exercise rather than fostering meaningful inclusion. For the HCI community, this moment calls for rethinking our approaches to participation, technology, ethics, and policy. In this meetup, we bring together researchers, practitioners, and advocates to revisit inclusive design through four themes: rethinking inclusive methodologies, disentangling technological challenges, unpacking ethical implications, and navigating policy opportunities. Through interactive mapping activities, participants will share practices, identify collaboration opportunities, and co-develop future directions. Our goal is to build cross-disciplinary connections and create actionable approaches that move beyond compliance toward holistic inclusion, ensuring that accessibility remains central to HCI research and practice.

The EmpathiCH workshop series has, over three iterations, unpacked how empathy is conceptualized, measured, and used in HCI, identifying both its potential benefits and notable pitfalls. Despite these discussions, the diverse roles of empathy in research and practice remain fragmented and under-theorized. This fourth iteration seeks to consolidate perspectives by situating empathy within a sociomaterial framework. We propose exploring three dimensions-technology, social practices, and context-that shape how empathy is conceptualized and applied. The workshop will combine an interactive, discussion-centric format enabling participants to share experiences, debate perspectives, and collaboratively analyze cases across these dimensions. Outcomes will contribute to co-developing a sociomaterial taxonomy for empathy in HCI, offering conceptual clarity and practical guidance for design. Participants will engage in critical dialogue, connect with peers, and contribute directly to shaping the future of empathy-centered approaches in HCI.

Large Language Model-powered conversational agents (CAs) are increasingly capable of projecting sophisticated personalities through language, but how these projections affect users is unclear. We thus examine how CA personalities expressed linguistically affect user decisions and perceptions in the context of charitable giving. In a crowdsourced study, 360 participants interacted with one of eight CAs, each projecting a personality composed of three linguistic aspects: attitude (optimistic/pessimistic), authority (authoritative/submissive), and reasoning (emotional/rational). While the CA's composite personality did not affect participants' decisions, it did affect their perceptions and emotional responses. Particularly, participants interacting with pessimistic CAs felt lower emotional state and lower affinity towards the cause, perceived the CA as less trustworthy and less competent, and yet tended to donate more toward the charity. Perceptions of trust, competence, and situational empathy significantly predicted donation decisions. Our findings emphasize the risks CAs pose as instruments of manipulation, subtly influencing user perceptions and decisions.

Battery-free computer gaming offers a vision of sustainable interaction in which games run on hardware that does not require a battery, yet this approach introduces uncertainty due to frequent power failures. Rather than viewing these failures as limitations, this work examines how integrating energy harvesting with application design can encourage users to reimagine and work with such failures, thus shaping behaviour and supporting device use. We present TURNER, a state-of-the-art modular battery-free games console powered by a hand crank and solar cells, created as a research probe to study how energy harvesting mediates the relationship between power and interaction. In a mixed-methods study (N = 60), we explored the influence of energy harvesting on gameplay. Findings show significant variations in harvesting strategies, with interviews surfacing strategies for creating applications that respond to and build on the patterns of system power failure, the ergonomics of energy harvesting, and the value of embedding energy generation into play. Our work offers insights for interactive, sustainable battery-free computers.

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.

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.

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.

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.

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.

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.

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 ‘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.

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.

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 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.