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In North America, bus operators are essential but undervalued public servants — the ''human infrastructure'' of public transit. Transit workers face a range of largely invisible health and safety issues that have worsened in recent years. As more attention is directed toward new technologies being commercialized in the sector these operational challenges remain largely unaddressed. Our paper contributes to a turn ''back to labor'' and describes issues bus operators face on the job. Through a diary study of bus operators' working conditions we detail howpunctuatedmoments of workplace violence, inhumane scheduling, and unsafe operational conditions becomeprolonged infrastructural failure.We outline how CSCW researchers and practitioners can contribute to the design of transit systems that enhance worker dignity and contribute to ongoing efforts to address urgent health and safety concerns. 

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. 

Sidewalk robots are becoming increasingly common worldwide, yet their operation on public walkways presents challenges for pedestrians. This is especially true for people with motor disabilities (PWMD) who already manage obstacles such as inadequate ramps and public incivility. The addition of sidewalk robots could further intensify these difficulties, which poses an urgent need to examine how the design of sidewalk robots may influence the daily navigation experiences of PWMD. This poster illustrates findings from semi-structured interviews with ten PWMD, providing insights into their perspectives on the presence of sidewalk robots. The study uncovers potential conflicts in shared sidewalk use and the adaptive actions PWMD described needing to undertake in response. Interviewees raised concerns about whether the robots could accommodate the needs of PWMD, as compared to people walking on foot, and the repercussions of any shortcomings in this regard. Our research also examines tensions stemming from different robotic design choices, indicating the necessity for more accessible public robot designs. We further delve into PWMD’s interaction needs and modalities for routine operation and in the event of robot malfunction. As cities increasingly allow for the deployment of robots in public spaces, this work seeks to inform equitable design and deployment guidelines for sidewalk robots and calls for further research into the implications of the rise of public robots for the diverse populations that make up any given municipality. 

Large generative AI models (GMs) like GPT and DALL-E are trained to generate content for general, wide-ranging purposes. GM content filters are generalized to filter out content which has a risk of harm in many cases, e.g., hate speech. However, prohibited content is not always harmful -- there are instances where generating prohibited content can be beneficial. So, when GMs filter out content, they preclude beneficial use cases along with harmful ones. Which use cases are precluded reflects the values embedded in GM content filtering. Recent work on red teaming proposes methods to bypass GM content filters to generate harmful content. We coin the term green teaming to describe methods of bypassing GM content filters to design for beneficial use cases. We showcase green teaming by: 1) Using ChatGPT as a virtual patient to simulate a person experiencing suicidal ideation, for suicide support training; 2) Using Codex to intentionally generate buggy solutions to train students on debugging; and 3) Examining an Instagram page using Midjourney to generate images of anti-LGBTQ+ politicians in drag. Finally, we discuss how our use cases demonstrate green teaming as both a practical design method and a mode of critique, which problematizes and subverts current understandings of harms and values in generative AI. 

Sidewalk delivery robots are being deployed as a form of last-mile delivery. While many such robots have been deployed on college campuses, fewer have been piloted on public sidewalks. Furthermore, there have been few observational studies of robots and their interactions with pedestrians. To better understand how sidewalk robots might integrate into public spaces, the City of Pittsburgh, Pennsylvania conducted a pilot of sidewalk delivery robots to understand possible uses and the challenges that could arise in interacting with people in the city. Our team conducted ethnographic observations and intercept interviews to understand how residents perceived of and interacted with sidewalk delivery robots over the course of the public pilot. We found that people with limited knowledge about the robots crafted stories about their purpose and function. We observed the robots causing distractions and obstructions with different sidewalk users (including children and dogs), witnessed people helping immobilized robots, and learned about potential accessibility issues that the robots may pose. Based on our findings, we contribute a set of recommendations for future pilots, as well as questions to guide future design for robots in public spaces.