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Algorithmic management is transforming traditional face-to-face service sectors like hospitality. To understand this phenomenon, we conducted an interview study in a unionized, mid-sized urban hotel on the West Coast of the USA. Through this work, we examine how an algorithmic management (AM) platform mediates work in a housekeeping department. Our analysis highlights the effects of AM on social processes, revealing that despite careful configuration, the tool’s implementation still challenges traditional communication and coordination. This study contributes empirical evidence on AM impacts in a collaborative service environment, emphasizing the importance of organizational dynamics in AM design and implementation. We offer design opportunities for flexible workplace technologies that support, rather than frustrate, the relational aspects of service work. 

Labor shortages have shaped many industries over the past several years, with hospitality experiencing one of the largest rates of attrition. Workers are leaving their jobs for a variety of reasons, ranging from burnout and work intensification to a lack of meaningful employment. While some literature maintains that labor-replacing automation is poised to bridge the shortages, we argue there is an opportunity for technology design to instead improve job quality and retention. Drawing on interviews with unionized guest room attendants, we report on workers’ perceptions of a widely-used algorithmic room assignment system. We then present worker-generated design ideas that adapt this system toward supporting three key facets of wellbeing: self-efficacy, transparency, and workload. We argue for the need to consider these facets of wellbeing through design across the service landscape, particularly as HCI attends to the impacts of AI and automation on frontline work. 

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. 

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.