Digital Twins in Healthcare for Citizens

Digital twins are gaining attention in healthcare, especially in fields like hospital management, simulating surgeries, or providing personalized health. As digital replicas based on users' data, digital twins can inform citizens in-depth about their lifestyle, medical data, and biomedical data. Hence, there is the assumption that digital twins could facilitate preventative healthcare at home, bringing healthcare closer to citizens, yet there are underexamined ethical concerns. In this paper, we explore the ethics of digital twins based on citizens' perspectives on digital twins in healthcare via recent literature and research. Although digital twins have great potential, citizens have concerns about surveillance, data ownership, data accuracy, and personal and collective agency

Human Mobility Trends during the COVID-19 Pandemic in the United States

In March of this year, COVID-19 was declared a pandemic and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations regarding the pandemic propagation and the non-pharmaceutical interventions. All mobility metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states and becomes more stable after the stay-at-home order with a smaller range of fluctuation. There exists overall mobility heterogeneity between the income or population density groups. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. The study suggests that the public mobility trends conform with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.Comment: 11 pages, 9 figure

Drawing as a Facilitator of Critical Data Discourse: Reflecting on Problems with Digital Health Data Through Expressive Visualizations of the Unseen Body Landscape

In a 1.5-hour workshop, we used drawing and self-reflection prompts to facilitate a value-driven discussion of personal and institutional data practices. Activities included mark-making in time with one's heartbeat, creating an inventory of one's personal data, and creating a qualitative personal health visualization. This article details the workshop structure and exercises and includes a summary of the discussion, which constructively encompassed both the empowering and the uncomfortable aspects of digital health data collection in a constructive manner. The workshop's design used the format of hands-on, expressive drawing activities to enable participants to achieve depth and breadth in a relatively short discussion about personal health, data autonomy, institutional trust, and consent. Critical discourse about data, especially health data, is a valuable experience for every person whose health data has been or is being collected; and approaches that take personal data as a starting point can support the practice of digital/data sovereignty more broadly

Mergen: The First Manchu-Korean Machine Translation Model Trained on Augmented Data

The Manchu language, with its roots in the historical Manchurian region of Northeast China, is now facing a critical threat of extinction, as there are very few speakers left. In our efforts to safeguard the Manchu language, we introduce Mergen, the first-ever attempt at a Manchu-Korean Machine Translation (MT) model. To develop this model, we utilize valuable resources such as the Manwen Laodang(a historical book) and a Manchu-Korean dictionary. Due to the scarcity of a Manchu-Korean parallel dataset, we expand our data by employing word replacement guided by GloVe embeddings, trained on both monolingual and parallel texts. Our approach is built around an encoder-decoder neural machine translation model, incorporating a bi-directional Gated Recurrent Unit (GRU) layer. The experiments have yielded promising results, showcasing a significant enhancement in Manchu-Korean translation, with a remarkable 20-30 point increase in the BLEU score.Comment: emnlp2023/mrl202

What's on your virtual mind?:mind perception in human-agent negotiations

Recent research shows that how we respond to other social actors depends on what sort of mind we ascribe to them. In this article we examine how perceptions of a virtual agent's mind shape behavior in human-agent negotiations. We varied descriptions and communicative behavior of virtual agents on two dimensions according to the mind perception theory:agency (cognitive aptitude) andpatiency (affective aptitude). Participants then engaged in negotiations with the different agents. People scored more points and engaged in shorter negotiations with agents described to be cognitively intelligent, and got lower points and had longer negotiations with agents that were described to be cognitively unintelligent. Accordingly, agents described as having low agency ended up earning more points than those with high agency. Within the negotiations themselves, participants sent more happy and surprise emojis and emotionally valenced messages to agents described to be emotional. This high degree of described patiency also affected perceptions of the agent's moral standing and relatability. In short, manipulating the perceived mind of agents affects how people negotiate with them. We discuss these results, which show that agents are perceived not only as social actors, but as intentional actors through negotiations

Participatory Design for Whom? Designing Conversational User Interfaces for Sensitive Settings and Vulnerable Populations

Conversational User Interfaces (CUIs) are becoming increasingly applied in a broad range of sensitive settings to address the needs and struggles of vulnerable or marginalized users. Sensitive settings include, for instance, CUIs mediating the communication difficulties of people with dementia or supporting refugees to cope with new cultural practices as a chatbot on a government website. While researchers are increasingly designing CUIs for such sensitive set tings, methods and participatory design approaches to address vulnerable user groups’ highly sensitive needs and struggles are sparse in research thus far. This workshop aims to explore how we can design CUIs for and in sensitive settings with vulnerable users in mind through the participatory design process. We aim to establish a working definition of vulnerability, sensitive settings, and how practice-oriented design of CUIs can be inclusive of diverse users

Designing with the more-than-human:Temporalities of thinking with care

This one-day workshop brings together HCI researchers, designers, and practitioners to engage with more-than-human temporalities in the context of designing with care. We invite participants to experiment and think with more-than-human time experiences as a starting point to integrate emergent methodologies and practices for more-than-human discourses in design. By using living and once-living media (e.g., fungi, plant and insect specimens, biodesigned artefacts) as starting points for investigating more-than-human temporalities, participants will discuss how a pluralistic temporal approach can offer to the discourse of designing-with nonhuman entities, and how this aligns with emerging HCI research trajectories and concerns