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.

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.

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.

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.

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.

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.

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.