Humanoid robots are increasingly expected to be designed to interact in diverse social contexts. Yet, recent commercial prototypes often omit expressive communication features, such as faces and gestures. This disconnect raises questions about how communication modalities are actually selected for real-world deployment. In this study, we interviewed 12 industry decision-makers involved in humanoid robot development to examine the factors that guide their design adoption choices. Our findings show that technical feasibility, cost, safety, reliability, and organizational priorities frequently outweigh the benefits of communication modalities. By highlighting where practitioner considerations align with or diverge from established design research, this study offers a grounded, industry-facing perspective on the design of humanoid communication. Interpreting our findings, we present a conceptual framework to guide design researchers in understanding decision-making for product development in industry contexts.

We examine how people experience two choices in the design of generative ghosts, AI systems that are trained on data of the dead: representation, where an AI speaks about a deceased person in the third person, and reincarnation, where the AI speaks as the deceased in the first person. Through a qualitative user study with 16 participants, we explore how each shaped authenticity, affect, and risk. Reincarnation was preferred for its immediacy, but participants shared fears of over-reliance. Representation was preferred for engaging with memory over conversational presence, though participants often ignored this distinction, engaging in dialogue despite third-person framing. Across both modes, participants privileged affective resonance over factual fidelity. We conclude by showing how factors such as tone, language, and conversational rhythm – factors unique to the user’s memory of the deceased – shape interactions with generative ghosts, and argue that those interactions are always collaborative.

Healthcare providers play a crucial role in developing care plans for patients at the end of their lives. As value elicitation becomes increasingly integrated into advance care planning, digital tools have been proposed to support providers. However, the development of these tools has primarily focused on facilitating communication of values from patients to providers. To address providers' specific needs and barriers, such as using values to create and manage medical care plans, we conducted an exploratory study capturing provider perspectives. Through interviews with 18 palliative care providers, we identified what they consider the typical value elicitation process, its associated challenges, and their visions for an ideal future process. To guide future research in determining priority areas for technology intervention, we synthesized our findings into a conceptual model that visualizes the tasks of providers in value elicitation. Comparing our model with previous models focused on patient and caregiver tasks, we found that provider perspectives introduce additional tasks in value elicitation that warrant attention: the creation, member-checking, and formal documentation of care plans by providers. Based on our findings, we argue that future work should focus on designing digital tools that support the provider-specific tasks of care plan creation and management to improve value elicitation.

Though online platforms have begun to include support for end-of-life needs, their development has been piecemeal and varies from platform to platform. Recent social computing research has examined end-of-life support on social media platforms and platforms specifically designed for grief and remembrance. However, understanding the functional end-of-life support provided by a wider array of platforms is needed to identify the most urgent design priorities beyond social media and remembrance-specific platforms. In this study, we present the results of a large-scale, multi-platform analysis of end-of-life support, summarizing the current state and identifying gaps as of April 2023. This study helps identify priorities to guide platform design and future research by identifying the current state of existing end-of-life support and gaps in that support.

Large language models (LLMs) are the new hot trend being rapidly integrated into products and services—often, in chatbots. LLM-powered chatbots are expected to respond to any number of topics, including topics central to gender identity . In light of rising anti-trans discourse, we examined how two popular LLMs responded to real-world English-language questions about trans identity taken from Quora. We employed reflexive analysis that centered our situated knowledges of the trans community. We found that LLMs return pro-trans responses, even when presented with highly transphobic user prompts. While we also found highly transphobic LLM responses, we found that anti-trans sentiment in LLMs was often subtle, requiring a deep positional understanding from diverse trans stakeholders to interpret. Based on these findings, we recommend diverging from current “value-neutral” approaches that validate transphobia by taking an “all sides” approach. We provide considerations for both the evaluation and design of LLMs that center positional expertise.

To support people at the end of life as they create management plans for their assets, planning approaches like estate planning are increasingly considering data. HCI scholarship has argued that developing more effective planning approaches to support end-of-life data planning is important. However, empirical research is needed to evaluate specific approaches and identify design considerations. To support end-of-life data planning, this paper presents a qualitative study evaluating two approaches to co-designing end-of-life data plans with participants. We find that asset-first inventory-centric approaches, common in material estate planning, may be ineffective when making plans for data. In contrast, heavily facilitated, mission-driven, relationship-centric approaches were more effective. This study expands previous research by validating the importance of starting end-of-life data planning with relationships and values, and highlights collaborative facilitation as a critical part of successful data planning approaches.

Clinical group bereavement therapy often promotes narrative sharing as a therapeutic intervention to facilitate grief processing. Increasingly, people turn to social media to express stories of loss and seek support surrounding bereavement experiences, specifically, the loss of loved ones from suicide. This paper reports the results of a computational linguistic analysis of narrative expression within an online suicide bereavement support community. We identify distinctive characteristics of narrative posts (compared to non-narrative posts) in linguistic style. We then develop and validate a machine-learning model for tagging narrative posts at scale and demonstrate the utility of applying this machine-learning model to a more general grief support community. Through comparison, we validate our model's narrative tagging accuracy and compare the proportion of narrative posts between the two communities we have analyzed. Narrative posts make up about half of all total posts in these two grief communities, demonstrating the importance of narrative posts to grief support online. Finally, we consider how the narrative tagging tool presented in this study can be applied to platform design to more effectively support people expressing the narrative sharing of grief in online grief support spaces.

When bereaved individuals seek online support in response to the suicide of a loved one, their expressions of grief take many forms. Although the intense grief expressions individuals bereaved by suicide commonly share in private therapeutic settings can be helpful in healing from traumatic loss, these same expressions may potentially cause harm to others when shared in a public online support community. In this study, we present a qualitative analysis of letters posted on the r/SuicideBereavement subreddit, and comments replying to those posts, to explore what diverse expressions of grief additionally demand of platform design. We find that letter posts contain potentially harmful grief expressions that, in this community, generate mutual support among community members. Informed by our findings, this study considers the design challenges for online platforms as they simultaneously support users receiving support and healing through sharing certain grief expressions, while also supporting users who will be harmed by exposure to those grief expressions. Taking inspiration from offline therapy modalities, we consider the design implications of creating specialized online grief support spaces for diverse grief expressions.

Access to mortality data is critical for researchers for various reasons, including the development of life-saving medical interventions. However, high-quality mortality data is typically unavailable for researchers to access in a timely manner due to bottlenecks and inefficiencies in the coordination of managing mortality data across a vast information ecosystem. To identify opportunities to improve access to mortality data, we conducted a qualitative interview study with 20 experts with direct knowledge and experience with mortality data systems. From these interviews, we mapped the mortality data ecosystem and found that policy constraints are the main underlying cause of bottlenecks and inefficiencies. We argue that policy intervention is required to optimize the coordination of mortality data between databases and between organizations. To support researcher access to mortality data, we present guiding principles for designers seeking to improve the mortality data ecosystem. These principles contribute to CSCW scholarship focused on the challenges of coordination across large information ecosystems and the tradeoffs between technology and policy when designing such systems.

Incorporating ethics into computing education has become a priority for the SIGCSE community. Many computing departments and educators have contributed to this endeavor by creating standalone computing ethics courses or integrating ethics modules and discussions into preexisting curricula. In this study, we hope to support this effort by reporting on computing educators' attitudes toward including ethics in their computing classroom, with a special focus on the structures that hinder or help this endeavor. We surveyed 138 higher education computing instructors to understand their attitudes toward including ethics in their classes, what barriers might be preventing them from doing so, and which structures best support them. We found that even though instructors were generally positive about ethics as a component of computing education, there are specific barriers preventing ethics from being included in some computing courses. In this work, we explore how to alleviate these barriers and outline support structures that could encourage further integration of ethics and computing in higher education.

To more effectively support the dying and bereaved in end-of-life contexts, over the past two decades HCI and social computing scholars have sought to understand digital legacy. In this paper, we argue that it is time to take stock of digital legacy scholarship, examining what we know, what gaps remain, and what areas are imperative for future work. Through a Grounded Theory Literature Review, we identify four foci in digital legacy research to date: how identity is navigated in the passing of digital legacy, how digital legacies are engaged with, how digital legacies are put to rest, and how technology interfaces with offline legacy technologies. Based on our analysis, we present a model depicting how digital legacy research examines a lifecycle of data as it is passed down. This model identifies that digital legacy data moves through three stages: encoding, accessing, and dispossessing. The model illustrates gaps in current research and charts possible inflection points for future social computing research. Specifically, we highlight the importance of multi-user and multi-generational networks of people in end-of-life scenarios. Additionally, the model exhibits emerging theoretical findings and major concepts in the nascent field of digital legacy research.

Technology plays an increasingly pivotal role in mediating mental health support in people's everyday lives. However, it is not clear how that mediation is occurring, to what end, and what technologies are implicated. In this study, we examine these questions with a mixed-methods analysis of conversations among participants in several Bipolar Disorder (BD) communities on Reddit. Analyzing posts produced over four years, we identify a wide variety of technologies that people employ to manage their mental conditions, such as communication technologies, online communities and tracking tools. Using this taxonomy of technologies as a framework, we then summarize three technology-mediated management strategies that these technologies enable, including serving as community, episode, and information mediators. We argue that with a comprehensive and nuanced understanding of people's in situ technology use, we can identify research and design opportunities for designing human-centered technologies to help people manage mental health challenges more effectively.