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What happens to a person’s data after they pass away? Designing for digital legacy requires input from individuals across all life stages, as motivations to plan vary with age. Yet, the specific perspectives that older adults have on end-of-life data management have not been investigated in depth. Through interviews with 16 older adults, we examine their preferences and motivations for managing everyday digital data (e.g., text messages, social media, photos) after death. Our findings surface several implications for end-of-life data planning, including creating awareness about digital legacy and associated risks. We also unpack and discuss how older adults’ life stage and familiarity with end-of-life planning uniquely positions them to identify barriers and opportunities in managing digital legacy, such as how post-mortem data can encode societal norms of a period or be donated for the greater good. 

As AI systems quickly improve in both breadth and depth of performance, they lend themselves to creating increasingly powerful and realistic agents, including the possibility of agents modeled on specific people. We anticipate that within our lifetimes it may become common practice for people to create custom AI agents to interact with loved ones and/or the broader world after death; indeed, the past year has seen a boom in startups purporting to offer such services. We call these generative ghosts since such agents will be capable of generating novel content rather than merely parroting content produced by their creator while living. In this paper, we reflect on the history of technologies for AI afterlives, including current early attempts by individual enthusiasts and startup companies to create generative ghosts. We then introduce a novel design space detailing potential implementations of generative ghosts. We use this analytic framework to ground a discussion of the practical and ethical implications of various approaches to designing generative ghosts, including potential positive and negative impacts on individuals and society. Based on these considerations, we lay out a research agenda for the AI and HCI research communities to better understand the risk/benefit landscape of this novel technology to ultimately empower people who wish to create and interact with AI afterlives to do so in a beneficial manner. 

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. 

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. 

The rapid development and deployment of generative AI technologies creates a design challenge of how to proactively understand the implications of productizing and deploying these new technologies, especially with regard to negative design implications. This is especially concerning in CSCW applications, where AI agents can introduce misunderstandings or even misdirections with the people interacting with the agent. In this panel, researchers from academia and industry will reflect on their experiences with ideas, methods, and processes to enable designers to proactively shape the responsible design of genAI in collaborative applications. The panelists represent a range of different approaches, including speculative fiction, design activities, design toolkits, and process guides. We hope that the panel encourages a discussion in the CSCW community around techniques we can put into practice today to enable the responsible design of genAI. 

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. 

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. 

This paper considers how subjectivities are enlivened in algorithmic systems. We first review related literature to clarify how we see “subjectivities” as emerging through a tangled web of processes and actors. We then offer two case studies exemplifying the emergence of algorithmic subjectivities: one involving computational topic modeling of blogs written by parents with children on the autism spectrum, and one involving algorithmic moderation of social media content. Drawing on these case studies, we then articulate a series of qualities that characterizes algorithmic subjectivities. We also compare and contrast these qualities with a number of related concepts from prior literature to articulate how algorithmic subjectivities constitutes a novel theoretical contribution, as well as how it offers a focal lens for future empirical investigation and for design. In short, this paper points out how certain worlds are being made and/or being made possible via algorithmic systems, and it asks HCI to consider what other worlds might be possible. 

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. 

AI technologies are likely to impact an array of existing practices (and give rise to a host of novel ones) around end-of-life planning, remembrance, and legacy in ways that will have profound legal, economic, emotional, and religious ramifications. At this critical moment of technological change, there is an opportunity for the HCI community to shape the discourse on this important topic through value-sensitive and community-centered approaches. This workshop will bring together a broad group of academics and practitioners with varied perspectives including HCI, AI, and other relevant disciplines (e.g., law, economics, religious studies, etc.) to support community-building, agenda-setting, and prototyping activities among scholars and practitioners interested in the nascent topic of how advances in AI will change socio-technical practices around death, remembrance, and legacy.