Vision-Language Models (VLMs) are increasingly used by blind and low-vision (BLV) people to identify and understand products in their everyday lives, such as food, personal care items, and household goods. Despite their prevalence, we lack an empirical understanding of how common image quality issues—such as blur, misframing, and rotation—affect the accuracy of VLM-generated captions and whether the resulting captions meet BLV people’s information needs. Based on a survey of 86 BLV participants, we develop an annotated dataset of 1,859 product images from BLV people to systematically evaluate how image quality issues affect VLM-generated captions. While the best VLM achieves 98% accuracy on images with no quality issues, accuracy drops to 75% overall when quality issues are present, worsening considerably as issues compound. We discuss the need for model evaluations that center on disabled people’s experiences throughout the process and offer concrete recommendations for HCI and ML researchers to make VLMs more reliable for BLV people.

Although knowledge workers are increasingly able to adopt remote and hybrid working arrangements and work productively, many organizations continue to question the effectiveness of remote work and focus on its concerns and challenges. Previous CSCW research shows that remote workers have limited awareness of other workers, require more explicit coordination, and feel excluded from in-person colleagues. Research also shows that adopting work practices and technologies that are remote work-friendly can offset many of these challenges. Identifying which effective practices and challenges are most helpful or hurtful to remote workers-and how workplace attributes (e.g., team structure; communication frequency; tool use) affect them-could strengthen organizations' strategies and policies for remote work. Through a theoretically-informed survey of 1,526 U.S. knowledge workers, we find many factors prior research has argued as essential to remote work, such as knowing your teammates personally, to be the least important for remote workers, and show how workplace attributes influence those perceptions. We provide theoretical and practical implications for future research for organizations that wish to support remote and hybrid work modalities.

Ill-structured problems demand that people adopt sophisticated strategies for planning, seeking support, and using available resources along their work process. These practices involve a challenging monitoring and strategizing process that existing tools cannot support since they largely lack an understanding of an organization’s processes, social structures, venues, and tools. We introduce workplace programming for situationally-aware systems–an approach for encoding work situations using computational abstractions of an organization’s ways of working and surfacing support strategies at appropriate times and settings. With this approach, we implement Orchestration Scripts, a system that supports various situated work activities in a socio-technical organization. Through a case study and field study, we show how our approach encodes different aspects of working effectively and helps people identify situations to enact effective strategies using the available support opportunities. Our results show how a programmable technology can provide situated support in today’s workplaces.

Work and learning communities have become increasingly networked to support their members in developing the skills to solve complex, real-world problems. Though disciplinary knowledge remains important to tackle these problems, working effectively in these modern-day communities of practice demands the ability for one to learn how to access networked support (e.g., venues, tools, resource guides, or peers) throughout the community for one's needs. Against this backdrop, we study networked orchestration--how community members access and learn to access networked supports--in a community of practice for undergraduate research training. Through field observations and in-depth interviews, we find that students in the networked research community dynamically engage with their mentors and peers across multiple venues throughout the week in order to identify, clarify, and resolve their needs. Mentors in the community monitor how students are engaging with the supports available in the network, and provide coaching on effective strategies when students are ineffective on their own. Finally, we surface the challenges involved in each of these processes and offer practical insights for future ecosystem-level networked orchestration technologies that have an understanding of the interactions occurring across the venues and tools in a community, and can support the learning and practice of effective access strategies. Our paper presents important insights for supporting people's work and learning needs in networked future workplaces and learning communities, and provides guidance on designing new technologies for supporting networked ways of working and learning.

Despite many available social technologies for connecting at a distance, we don't always find opportunities to actively engage in shared experiences and activities with friends and loved ones, even though this kind of interaction is associated with increased social closeness. To better support active engagement in shared experiences and activities while also making it convenient to find opportunities for interacting in this way, our work explores the design of Opportunistic Collective Experiences (OCEs), or social experiences powered by computer programs that identify opportune moments when users share situations across distance and structure shared activities in those situations. To support interacting with, programming, and executing OCEs, we developed Cerebro, a computational platform that consists of a mobile app that supports users? social interaction, an API for expressing the situations and activities that make up the interactional opportunity, and an opportunistic execution engine that checks for interactional opportunities and executes them when possible. Through a 20 day deployment study tested with groups of geographically-distributed college alumni (N=21), we found that OCEs promoted opportunities for active engagement; facilitated interactions that were socially connecting by structuring ways to engage in shared experiences and activities; and made actively engaging easier by identifying situations appropriate for interacting and structuring how to engage in activities in these situations. We contribute to CSCW (1) a novel interaction that facilitates engaging in shared experiences and activities at distance during coincidental moments; and (2) the design of systems to interact with, program, and execute these kinds of interactions.

Participatory sensing systems in which people actively participate in the data collection process must account for both the needs of data contributors and the data collection goals. Existing approaches tend to emphasize one or the other, with opportunistic and directed approaches making opposing tradeoffs between providing convenient opportunities for contributors and collecting high-fidelity data. This paper explores a new, hybrid approach, in which collected data-even if low-fidelity initially-can provide useful information to data contributors and inspire further contributions. We realize this approach with 4X, a multi-stage data collection framework that first collects data opportunistically by requesting contributions at specific locations along users' routes and then uses collected data to direct users to locations of interest to make additional contributions that build data fidelity and coverage. To study the efficacy of 4X, we implemented 4X into LES, an application for collecting information about campus locations and events. Results from two field deployments (N = 95, N = 18) show that the 4X framework created 34% more opportunities for contributing data without increasing disruption, and yielded 49% more data by directing users to locations of interest. Our results demonstrate the value and potential of multi-stage, dynamic data collection processes that draw on multiple sources of motivation for data, and how they can be used to better meet data collection goals as data becomes available while avoiding unnecessary disruption.