Advances in artificial intelligence (AI) offer the potential for chatbots to support public health monitoring by automating tasks traditionally performed by frontline workers. While introducing AI impacts public agency workers across decision-making, administration, and monitoring roles, the perceptions of workers regarding these technologies and their actual impact on labor are underexplored. We examine the case of CareCall, a large language model (LLM)-driven chatbot used to monitor socially isolated individuals, by interviewing 21 public agency workers across 13 sites involved in its adoption and rollout. We find that CareCall helped expand public reach but increased burdens on frontline workers due to insufficient resources and new labor demands, such as handling lapses in user engagement. We discuss how implementing LLM-driven chatbots in public health contexts can complicate decision-makers' articulation work and impose additional maintenance work on frontline workers. We recommend AI chatbots in this space leverage public infrastructure and incorporate fallback mechanisms.
California’s agricultural workers are a vulnerable population due to their undocumented status and poor working conditions. This paper describes community engagement with NGO workers, farm laborers, and farm owners to identify and address the effects of climate change, namely heat stress, on, strawberry field workers. We deployed personal informatics devices in a longitudinal study with three field workers for a month and a half and presented the collected statistics back to them, asking them to reflect on their personal health (e.g., exposure to heat stress) and work data. We found that field workers normalized grit - the irregularity, adversity, competitiveness, and helplessness of their labor - thereby limiting the promise of personal informatics to help users lead healthier lives. Implicitly, personal informatics supports white collar workers such as information workers; overall, however, our study suggests a mismatch between current designs and front-line work which involve intensive physical work requirements.
The COVID-19 pandemic temporarily disrupted the operations of on-demand ride-sourcing digital labour platforms like Uber and Ola, severely impacting gig workers' labour opportunities. In response, the Kolkata Ola-Uber App-Cab Operator and Drivers Union in West Bengal, India, mobilised an alternate socio-technical infrastructure by operating emergency transport and taxi ambulance services. Our ethnographic study explores how this initiative leveraged technologies to structure and coordinate hybrid sites of action and ‘generate’ a labour market without profit motive to support the public health infrastructure. Our paper highlights the significance of what we call the gig worker union's ‘generative politics’ in creating resources to support workers and citizens, facilitating political action beyond protest politics, contributing to new counter-hegemonic formations, and shaping collective action centered around regeneration and care for the city and life under capitalism. We contribute to the HCI literature by offering insights to design alternate and participatory socio-technical infrastructures that challenge the hegemony of digital labour platforms.
Contact tracing has shown to be an effective tool in limiting the spread of transmittable diseases in countries where it is widely adopted. During the COVID-19 pandemic, contact tracing app adoption in the United States was low despite having the highest number of recorded cases worldwide. To better understand why, we conducted a survey (N=302, matched to U.S. census demographics) and found that political orientation overwhelmingly predicted attitudes towards COVID-19 and the adoption of contact tracing apps. These attitudes also overwhelmingly shaped people's willingness to participate in contact tracing for diseases in future pandemics. Our findings suggest that the politically charged environment surrounding COVID-19 in the U.S. may have a long-term impact on American's willingness to utilize contact tracing for diseases in future pandemics. We conclude with recommendations for technology designers and policymakers on how to overcome the sharp divide that has been driven by the political discourse in the U.S.
Nighttime sidewalk illumination has a significant and unequal influence on where and whether pedestrians walk at night. Despite the importance of pedestrian lighting, there is currently no approach for measuring and communicating how humans experience nighttime sidewalk light levels at scale. We introduce NightLight, a new sensing approach that leverages the ubiquity of smartphones by re-appropriating the built-in light sensor ---traditionally used to adapt screen brightness---to sense pedestrian nighttime lighting conditions. We validated our technique through in-lab and street-based evaluations characterizing performance across phone orientation, phone model, and varying light levels demonstrating the ability to aggregate and map pedestrian-oriented light levels with unaltered smartphones. Additionally, to examine the impact of light level data on pedestrian route choice, we conducted a qualitative user study with 13 participants using a standard map vs. one with pedestrian lighting data from NightLight Our findings demonstrate that people changed their routes in preference of well-light routes during nighttime walking. Our work has implications for expanding personalized navigation and pedestrian route choice and passive urban sensing.