Artificial Intelligence (AI) and Machine Learning (ML) technologies are increasingly integrated into a variety of products and services. However, AI promises often fall short and unintended consequences multiply, causing a negative impact, especially on marginalised communities. In response, Participatory AI has emerged as a promising method to account for the consequences on people subject to AI and to help develop systems that are better aligned with societal values. Yet meaningful participation remains difficult to achieve. Participation tends to be short-term and consultative, or even a mask for hidden labour. Moreover, the aim of building knowledge that is situated and subjective often conflicts with the global scale and generalizability of AI, especially when it comes to foundational models. This panel unites experts to explore how to account for these issues, and more specifically, to collectively draw a picture of the limits of Participatory AI in terms of social justice.

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.

Gender microaggressions are subtle yet persistent forms of discrimination in workplace interactions. While LLMs can detect them in written texts, it remains poorly understood how their interpretations align or diverge from human perspectives and experiences. We present a mixed-method study comparing how LLMs and humans differing in gender identity and lived experience, interpret gender microaggressions in the workplace. Using short dialogues adapted from real-world accounts, we asked 141 participants to rate the likelihood that a scenario contains a microaggression and provide a rationale for their answers. The same tasks were completed by 7 different LLM models. Our analysis reveals significant differences in how humans and LLMs interpret microaggressions, captured in both ratings and rationales, and more interestingly, the effect of gender and lived experience on human interpretations. These findings highlight the need for systems detecting microaggressions to embrace interpretive plurality, and support reflection and awareness while accounting for ambiguity.

AI alignment relies on annotator judgments, yet annotation pipelines often treat annotators as interchangeable, obscuring how their social position shapes annotation. We introduce reflexive annotating as a probe that invites crowd workers to reflect on how their positionality informs subjective annotation judgments in a language model alignment context. Through a qualitative study with crowd workers (N = 30), including follow-up interviews (N = 5), we examine how our probe shapes annotators' behaviour, experience, and the situated metadata it elicits. We find that reflexive annotating captures epistemic metadata beyond static demographics by eliciting intersectional reasoning, surfacing positional humility, and nudging viewpoint change. Crucially, we also denote tensions between reflexive engagement and affective demands such as emotional exposure. We discuss the implications of our work for richer value elicitation and alignment practices that treat annotator judgments as situated and selectively integrate positional metadata.

Cancer treatment leaves survivors with sexual difficulties that extend beyond physical symptoms and permeate many aspects of life, yet these concerns remain neglected in current cancer care. This paper responds to this gap by exploring how bespoke co-designed care technologies can support survivors when grounded in their lived sexual experiences. We conducted trauma-informed, generative workshops with two cancer survivors. The workshops surfaced four themes: gaps in anticipatory care, shifts from lovers to carers, unsettled bodies and selfhood, and navigating fragmented support. Through co-designing, we created Lived Experiences Archive (a ĝzine series of anonymous survivor stories) and BodyTalk (a sensory couple game for rebuilding emotional and physical intimacy). Beyond the artefacts, we contribute a methodological account of co-designing as care and empirical insights into post-cancer sexuality. We demonstrate the epistemic potential of bespoke intimate health technologies to generate situated forms of care and knowledge often overlooked in conventional health technology design.

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.

Anticipating the ethical and societal risks of emerging technologies has become an urgent challenge as their rapid integration into everyday life can produce far-reaching social consequences. In response, Design Futures practices are gaining traction within HCI and design as approaches to critically examine and anticipate the implications of technology. Yet, systematic knowledge on how these practices are structured to foster ethical reflection remains limited. To address this gap, we conducted a scoping review of 32 case studies employing Design Futures to engage with ethical concerns. Drawing from this review, we present a Taxonomy of Design Futures Processes for Ethical Reflection, which illustrates how different activities, actors' involvement, and types of futures generated shape the scope of ethical discussion. This taxonomy provides researchers and practitioners with practical guidance for creating Design Futures activities that foster ethical reflection on technology.

The increasing digitisation that we have witnessed in the past few years has resulted in increased information and communications technology (ICT) hardware manufacturing, which is not sustainable due to the growing demand for critical materials and the greenhouse emissions associated with it. A solution is transitioning to a circular economy (CE). To facilitate this, boost the data economy and digital innovation, the European Union has introduced digital product passports (DPPs), which should provide information about a product’s lifetime to bring more transparency into supply chains. However, several challenges, namely the lack of findable, accessible, interoperable, reusable ICT and materials data and tools to support its interpretation for decision-making, are present. Utilising ontologies and knowledge graphs is a possible solution. Although the ontology work in the ICT and materials domains has been on the rise, there is a lack of a unified semantic model that can capture the complex, heterogeneous cross-domain data needed for building DPPs of ICT devices such as laptops and data servers. Motivated by this, we present the RePlanIT ontology for ICT DPPs, which captures knowledge on several levels – ICT device, hardware components, materials and the CE itself. RePlanIT’s specification is based on a literature survey, interviews and inputs from domain experts from both industry and academia. The ontology, its utilisation for building a knowledge graph of DPPs of laptops and data servers and its application have been successfully validated in a real-world case focusing on supporting more sustainable ICT procurement in government.

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.