David R. Karger

Enterprise API design is often bottlenecked by the tension between rapid feature delivery and the rigorous maintenance of usability standards. We present an industrial case study evaluating an AI-assisted design workflow trained on API Improvement Proposals (AIPs). Through a controlled study with 16 industry experts, we compared AI-generated API specifications against human-authored ones. While quantitative results indicated AI superiority in 10 of 11 usability dimensions and an 87% reduction in authoring time, qualitative analysis revealed a paradox: experts frequently misidentified AI work as human (19% accuracy) yet described the designs as unsettlingly “perfect.” We characterize this as a “Perfection Paradox”—where hyper-consistency signals a lack of pragmatic human judgment. We discuss the implications of this perfection paradox, proposing a shift in the human designer’s role from the “drafter” of specifications to the “curator” of AI-generated patterns.

Researchers have characterized contexts for information work that are supported by often-messy ecosystems of information systems. Although important infrastructures for information work, little is known about how these less-than-perfect systems affect the data that is managed. We present results of an interview study of these information ecosystems in the nonprofit context. We find that the quality of data is often systematically degraded in five ways: incomplete data, out-of-date data, “bulk” data, “anecdotal" numbers, and “garbage" data. These forms of degraded data result from informants having other, legitimate priorities in their work—each more important than managing data. We discuss how degraded data often has little effect on organizations’ existing data practices, but forecloses alternate possible uses moving forward. Finally, we reflect on how we might be able to address these challenges while still respecting the legitimacy of choosing other priorities and explore what it would mean to design for a data imagination.

Social annotation platforms enable student engagement by integrating discussions directly into course materials. However, in large online courses, the sheer volume of comments can overwhelm students and impede learning. This paper investigates community-based design interventions on a social annotation platform (NB) to address this challenge and foster more meaningful online educational discussions. By examining student preferences and reactions to different curation strategies, this research aims to optimize the utility of social annotations in educational contexts. A key emphasis is placed on how the visibility of comments shapes group interactions, guides conversational flows, and enriches learning experiences. The study combined iterative design and development with two large-scale experiments to create and refine comment curation strategies, involving thousands of students. The study introduced specific features of the platform, such as targeted comment visibility controls, which demonstrably improved peer interactions and reduced discussion overload. These findings inform the design of next-generation social annotation systems and highlight opportunities to integrate Large Language Models (LLMs) for key activities like summarizing annotations, improving clarity in student writing, and assisting instructors with efficient comment curation.

While the benefits of social technologies have been profound, the harms too have cast a long shadow. This panel follows a workshop hosted by the Computing Community Consortium (CCC) Workshop "The Future of Research on Social Technologies'' held in November 2023. The workshop asked two guiding questions: "What should we know about social technologies, and what is needed to get there?'' This panel will discuss some key themes that arose. First, is decentralization a viable and desirable model? Second, how will AI impact online conversations? Third, how should we combat threats to researchers studying risky or contentious topics? Last, are we publishing too much while having too little impact? Panelists will present their own stances and then discuss and debate where the field should go.

When people need help from their supervisors or peers, they often have to manage up to get things done. However, unlike managing subordinates (managing down), managing people of equal or higher status (managing up) are not obligated to help. These requests often involve collaborative tasks between requesters and performers. Through interviews, we found that these collaborative tasks require coordination work that is not materialized in existing management tools. We also found that requesters are willing to take on this coordination work to see their requests fulfilled. To address this issue, we propose a system called TaskLight , which allows requesters to handle coordination work themselves. For example, requesters can collect useful context and information for their performers. We conducted two deployment studies and found that TaskLight leads to better outcomes because requesters are able to assist performers more effectively. Our findings demonstrate a new way to reduce the social burdens of managing up and improve collaboration.

In communities with social hierarchies, fear of judgment can discourage communication. While anonymity may alleviate some social pressure, fully anonymous spaces enable toxic behavior and hide the social context that motivates people to participate and helps them tailor their communication. We explore a design space of meronymous communication, where people can reveal carefully chosen aspects of their identity and also leverage trusted endorsers to gain credibility. We implemented these ideas in a system for scholars to meronymously seek and receive paper recommendations on Twitter and Mastodon. A formative study with 20 scholars confirmed that scholars see benefits to participating but are deterred due to social anxiety. From a month-long public deployment, we found that with meronymity, junior scholars could comfortably ask “newbie” questions and get responses from senior scholars who they normally found intimidating. Responses were also tailored to the aspects about themselves that junior scholars chose to reveal.