Mark S. Ackerman

BACKGROUND There are increasing interests in developing AI tools to identify and address individual-level social determinants of health in both health care and human service settings. These activities are part of social care integration, which at the individual level involves identifying individuals with social risks (awareness) and connecting them with relevant social care resources (assistance). Social care providers such as community health workers and social workers are deemed critical stakeholders in both settings. Chatbots have shown feasibility and acceptability for social risk screening in emergency departments and primary care centers. However, we do not know if a screening chatbot is worth developing for the safety net health care and human service settings, where there are visitors with greater social needs and less organizational resources. OBJECTIVE The study aims to investigate the perceived value proposition of an AI-based chatbot for social risk screening from the perspectives of social care providers in safety net health care and human service organizations. Providers’ perceived value propositions of other AI-based applications for social care integration were also examined. METHODS We conducted semi-structured interviews with 19 social care providers who have experience with awareness and/or assistance from 16 safety net health care and human service organizations in Michigan. Interview questions focused on their experiences and challenges regarding awareness and assistance when applicable. A simulated screening chatbot based on ChatGPT-4o was also used to solicit their feedback on the technology. The nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework was used to guide data analysis. Interview transcripts were first coded deductively, then inductively. RESULTS Social care providers perceived the screening chatbot as offering limited value. This is mainly because many participants engaged in assistance activities and they noted addressing social needs is a multi-step process requiring follow-up that screening chatbots do not provide. In addition, they valued cultivating trust as many patients/clients have high social needs and lack trust in the health care system, and felt chatbots present new challenges for maintaining essential trust and care quality with clients/patients. Instead of a screening chatbot, we identified that technologies to reduce documentation burden could improve providers’ efficiency and potentially increase time spent with patients/clients. This is because social risks and needs documentation generates administrative burden for providers. We also found that technologies to improve referral accuracy and engagement could improve providers’ effectiveness, as study participants have overall limited access to technologies that typically support referral-related activities. CONCLUSIONS Social care providers in the safety net preferred AI-based applications for addressing documentation burden and social needs assistance rather than for social risk screening. Future strategies to develop AI tools for social care integration should align with social care providers’ professional values and focus on equity-centered care.

U.S. public housing is historically a site of racialized and carceral surveillance. Digital surveillance technologies reinforce this containment by mediating carceral visuality, the institutionalized visual and analytic lenses that shape perceptions and governance of public housing spaces and residents. This paper presents a videovoice project with a public housing community, for which residents used smartphones to capture their routines, spatial practices, and imaginaries in relation to surveillance. We analyze how these video artifacts enact alternative ways of seeing and knowing, surfacing overlooked routines, relations, and critiques of surveillance. These videos document what is often invisibilized: the lived consequences of carceral visuality and the situated knowledge of those surveilled. We propose “counter-visual artifacts” to describe the political and disruptive role these videos play in challenging dominant visual regimes and reclaiming the right to see and be seen otherwise. By advocating for counter-visual sensibilities, we invite HCI scholars to rethink how artifacts make room for alternative ways of seeing.

Research on public attitudes toward science and technology policy has relied on surveys taken at single points in time. These surveys fail to indicate how these attitudes develop or change. In this study, we use data from the Longitudinal Study of American Life that has followed a national sample of Generation X for 33 years—from middle school to midlife. We demonstrate that the critical period for the formation of attitudes toward science and technology is the 15–18 years after high school—college, work, family, and career. The attitudes formed in this period remain stable for most individuals during midlife. This work provides an important perspective for scientists, engineers, and the leadership of the scientific community in their efforts to foster positive attitudes toward science and technology and to understand the roots of concerns and reservations about science.

Social media systems are as varied as they are pervasive. They have been almost universally adopted for a broad range of purposes including work, entertainment, activism, and decision making. As a result, they have also diversified, with many distinct designs differing in content type, organization, delivery mechanism, access control, and many other dimensions. In this work, we aim to characterize and then distill a concise design space of social media systems that can help us understand similarities and differences, recognize potential consequences of design choice, and identify spaces for innovation. Our model, which we call Form-From, characterizes social media based on (1) the form of the content, either threaded or flat, and (2) from where or from whom one might receive content, ranging from spaces to networks to the commons. We derive Form-From inductively from a larger set of 62 dimensions organized into 10 categories. To demonstrate the utility of our model, we trace the history of social media systems as they traverse the Form-From space over time, and we identify common design patterns within cells of the model.

The public acceptance of evolution remains a contentious issue in the United States. Numerous investigations have used national cross-sectional studies to examine the factors associated with the acceptance or rejection of evolution. This analysis uses a 33-year longitudinal study that followed the same 5000 public-school students from grade 7 through midlife (ages 45–48) and is the first to do so in regard to evolution. A set of structural equation models demonstrate the complexity and changing nature of influences over these three decades. Parents and local influences are strong during the high school years. The combination of post-secondary education and occupational and family choices demonstrate that the 15 years after high school are the switchyards of life.