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HRI scholars envision a future of work where human-robot collaboration brings mutual gains: organizations benefit from increased efficiency and productivity, and laborers benefit when tasks are redistributed between humans and robots based on their respective strengths. Yet, ironically, this collaboration in real-world contexts can lead to the opposite effect-workers' efficiency may decrease due to the additional tasks they must undertake to manage unexpected errors caused by robots. This “stop-gap” labor, often viewed as temporary and naturally manageable over time, can have significant and persistent impacts on workers. Drawing from observations across multiple robot deployment sites, this paper highlights the overlooked burden of this labor, challenging idealized visions of seamless human-robot collaboration. We argue that attending to stop-gap labor presents an opportunity for the HRI community to make genuine improvements for workers as primary stakeholders within complex socio-economic networks. 

With the onset of the COVID-19 pandemic in the United States, "essential work" became a calling card for the labor that kept the country running. But the activity of essential workers often occurs out of sight. For example, the products of waste workers are everywhere---clean floors, sanitized tables, objects made from recycled plastics---though workers themselves are often behind the scenes. 

This pictorial critically explores the role of visual media representations in the deployment of automated and artificially intelligent (AI) technologies within essential work sectors. We draw on an exhaustive review of local and national newspaper articles about automation in two waste labor industries (cleaning and recycling) over the last five years. We highlight a set of common visual tropes and move to challenge these representations by taking up the lens of countervisuality. Our analysis reveals that press photographs tend to focus on machines and the decision-makers who champion them, overlooking the work that it takes to integrate technology on the ground. Through our countervisuals, we depict the extensive efforts of waste workers to maintain AI technologies, and their potential for surveillance. Through visualizing under-recognized forms of labor that come after the design process ends, we highlight how an outsized emphasis on invention ignores waste workers’ expertise and needs over time. 

This paper examines the rapid introduction of AI and automation technologies within essential industries amid the COVID-19 pandemic. Drawing on participant observation and interviews within two sites of waste labor in the United States, we consider the substantial effort performed by frontline workers who smooth the relationship between robotics and their social and material environment. Over the course of the research, we found workers engaged in continuous acts of calibration, troubleshooting, and repair required to support AI technologies over time. In interrogating these sites, we develop the concept of patchwork: human labor that occurs in the space between what AI purports to do and what it actually accomplishes. We argue that it is necessary to consider the often-undervalued frontline work that makes up for AI's shortcomings during implementation, particularly as CSCW increasingly turns to discussions of Human-AI collaboration.