Jodi Forlizzi’s research while affiliated with Carnegie Mellon University and other places

Communication among some older adults is affected by cognitive and mobility impairments. This increases isolation, particularly for those residing in care homes, and leads to accelerated cognitive decline. Previous research has leveraged assistive robots to promote recreational routines and communication among older adults, with the robot leading the interaction. However, older adults could have more agency in the interaction, as robots could extend elders’ intentions and needs. Therefore, we explored an approach whereby the robot’s agency is shifted to the older adults who lead the interaction by commanding a robot’s actions using interactive physical blocks (tangible blocks). We conducted sessions with 22 care home dwellers where they could exchange messages and objects using the robot. Based on older adults’ observed behaviors during the sessions and perspectives gathered from interviews with geriatric professionals, we reflect on the opportunities and challenges for increased user agency and the asymmetries that emerged from differing abilities and personality traits. Our qualitative results highlight the potential of robotic approaches to extend the agency and communication of older adults, anchored on human values, such as the exchange of affection, collaboration, and competition.

The Future of Design Education working group on doctoral education included doctoral supervisors from nine programs around the world and addressed the indeterminacy of standards for the PhD in Design. Internationally, “contributions to knowledge” under the PhD degree title range from evidence-based investigations documented in a dissertation to personal reflections on making artifacts. In some programs, quantitative and qualitative research methods are taught; in others, there is no instruction in methods. The working group suggested that reflection on one’s own creative production is the role of the professional master’s degree and recommended standards for two doctoral programs—the PhD and the Doctor of Design (DDes). The group defined the PhD as addressing unresolved problems with the goal of generalizable knowledge or theory for the field. It described the DDes as a professional practice degree in which research is done in a practice setting to frame a specific opportunity space, guide in-process design decisions, or evaluate outcomes. DDes findings do not claim generalizability and result in “cases.” The working group discussed methods, sampling, standards of evidence and claims, ethics, research writing, and program management.

Recent investments in automation and AI are reshaping the hospitality sector. Driven by social and economic forces affecting service delivery, these new technologies have transformed the labor that acts as the backbone to the industry-namely frontline service work performed by housekeepers, front desk staff, line cooks and others. We describe the context for recent technological adoption, with particular emphasis on algorithmic management applications. Through this work, we identify gaps in existing literature and highlight areas in need of further research in the domains of worker-centered technology development. Our analysis highlights how technologies such as algorithmic management shape roles and tasks in the high-touch service sector. We outline how harms produced through automation are often due to a lack of attention to non-management stakeholders. We then describe an opportunity space for researchers and practitioners to elicit worker participation at all stages of technology adoption, and offer methods for centering workers, increasing transparency, and accounting for the context of use through holistic implementation and training strategies.

Significance To study the COVID-19 pandemic, its effects on society, and measures for reducing its spread, researchers need detailed data on the course of the pandemic. Standard public health data streams suffer inconsistent reporting and frequent, unexpected revisions. They also miss other aspects of a population’s behavior that are worthy of consideration. We present an open database of COVID signals in the United States, measured at the county level and updated daily. This includes traditionally reported COVID cases and deaths, and many others: measures of mobility, social distancing, internet search trends, self-reported symptoms, and patterns of COVID-related activity in deidentified medical insurance claims. The database provides all signals in a common, easy-to-use format, empowering both public health research and operational decision-making.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID- 19 activity, such as signals extracted from de-identified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data is available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Background: Individuals with life-limiting illnesses experience psychotherapeutic benefits of transmitting their life's history to loved ones; however, the scope and depth of what warrants preservation and who ought to undertake such activity remains less clear. Furthermore, individuals with conditions that afflict the brain face barriers regarding the timing and structure of such interventions. We analyzed data from an online social media forum to understand perceptions of legacy-making. Methods: This is a qualitative descriptive study of Slashdot, a social media website with a focus on science, technology, and politics. In August 2010, a Slashdot user inquired about a loved one with a life-limiting illness and asked for opinions on how to preserve the individual's memories. We conducted a content analysis of the individual comments related to digital legacy-making to identify common themes. Results: Slashdot users contributed 527 replies to the initial inquiry. Users often included bereaved individuals who offered input on the need to preserve information about a loved one, the modalities in which to preserve, and what type of content to preserve. Three key themes emerged related to legacy-making: (1) capture the individual's essence and avoid the minutia, (2) live for now to avoid prolonged suffering, and (3) recognize the equal benefits to all who memorialize. Conclusions: Users in a social media forum articulated the value of capturing their loved ones' essence for posterity, which many believed would help them to avoid prolonged grief. These findings have implications for the development and timing of personalized psychosocial interventions as well as informing application development of evidence-based digital legacy systems.