Subsidiarity is a principle of social organization that promotes human dignity and resists over-centralization by balancing personal autonomy with intervention from higher authorities only when necessary. Thus it is a relevant, but not previously explored, critical lens for discerning the tradeoffs between complete user control of software and surrendering control to “big tech” for convenience, as is common in surveillance capitalism. Our study explores data privacy through the lens of subsidiarity: we employ a multi-method approach of data flow monitoring and user interviews to determine the level of control different everyday technologies currently operate at, and the level of control everyday computer users think is necessary. We found that chat platforms like Slack and Discord violate subsidiarity the most. Our work provides insight into when users are willing to surrender privacy for convenience and demonstrates how subsidiarity can inform designs that promote human flourishing.
Ineffective meetings are pervasive. Thinking ahead explicitly about meeting goals may improve effectiveness, but current collaboration platforms lack integrated support. We tested a lightweight goal-reflection intervention in a preregistered field experiment in a global technology company (361 employees, 7196 meetings). Over two weeks, workers in the treatment group completed brief pre-meeting surveys in their collaboration platform, nudging attention to goals for upcoming meetings. To measure impact, both treatment and control groups completed post-meeting surveys about meeting effectiveness. While the intervention impact on meeting effectiveness was not statistically significant, mixed‑methods findings revealed improvements in self‑reported awareness and behaviour across both groups, with post‑meeting surveys unintentionally functioning as an intervention. We highlight the promise of supporting goal reflection, while noting challenges of evaluating and supporting workplace reflection for meetings, including workflow and collaboration norms, and attitudes and behaviours around meeting preparation. We conclude with implications for designing technological support for meeting intentionality.
Knowledge workers spend significant time finding, understanding, and relating information from different web pages. This process often leads to cluttered workspaces requiring active management of web pages, their evolving relevance, and relationships. Existing approaches, such as bookmarking systems, are often too rigid and impose considerable cognitive effort to create and maintain. In this work, we identify key design concepts to capture and retrieve the emerging and dynamic structures of web pages. In a field experiment with 29 knowledge workers (primarily students and IT professionals), we investigated current web management practices over time before we deployed our technology probe (Gstell) to explore initial behavioral adaptation to our design concepts in practice. Our analysis showed that many participants frequently struggle with excessive but inactive browser tabs and that the design concepts can alleviate the overload and improve focus. We discuss design considerations for more sustainable web page management.
An increasing number of HCI researchers have embraced open science ideas, such as sharing data and analysis code. However, such practices are only meaningful when the shared data and code enable other researchers to reproduce and reuse the reported findings. To investigate the reproducibility of HCI research, we identified all CHI papers that have shared study data and analysis code, and attempted to reproduce the results. We were able to fully reproduce 49\% of the papers. We surveyed and interviewed authors, asking them to assess the reproducibility of their own work and to reflect on their motivations and obstacles in doing open science. We discuss what improves and hinders reproducibility and provide recommendations on how to increase reproducibility rates in HCI. While the value of replicability remains contested in HCI, we argue that the more modest goal of reproducibility is desirable.
Explainable AI (XAI) is frequently positioned as a technical problem of revealing the inner workings of an AI model. This position is affected by unexamined onto-epistemological assumptions: meaning is treated as immanent to the model, the explainer is positioned outside the system, and a causal structure is presumed recoverable through computational techniques. In this paper, we draw on Barad’s agential realism to develop an alternative onto-epistemology of XAI. We propose that interpretations are material-discursive performances that emerge from situated entanglements of the AI model with humans, context, and the interpretative apparatus. To develop this position, we read a comprehensive set of XAI methods through agential realism and reveal the assumptions and limitations that underpin several of these methods. We then articulate the framework’s ethical dimension and propose design directions for XAI interfaces that support emergent interpretation, using a speculative text-to-music interface as a case study.
Social media has billions of users, but we still do not fully understand why users prefer one platform over another. Establishing new platforms among already popular competitors is difficult. Prior research has richly documented people's experiences within individual platforms, yet situating those experiences within the entirety of a user's social media experience remains challenging. What platforms have people used, and why have they transitioned between them? We collected data from a quota-based sample of 1,000 U.S. participants. We introduce the concept of \emph{Social Media Journeys} to study the entirety of their social media experiences systematically. We identify push and pull factors across the social media landscape. We also show how different generations adopted social media platforms based on personal needs. With this work, we advance HCI by moving towards holistic perspectives when discussing social media technology, offering new insights for platform design, governance, and regulation.
This paper investigates data repair practices through a six-month-long ethnographic study in Bangladesh. Our interviews and field observations with data repairers and related stakeholders found that, alongside the scarcity of high-precision machinery and access to advanced software, data repair work is constrained by cross-language learning resources and the protective nature of documenting, curating, and sharing the experiences and knowledge among local peers. Repairers turning to external resources such as foreign forums and LLMs also revealed their frustrating experiences and the postcolonial ethical tensions they encountered. We noted that both anticipated technical labor and the emotionality of data were taken into account for pricing the data repair job, which contributed to their market sustainability strategies. Engaging with repair, infrastructure, and data poverty discourse, we argue that data repair practices represent a crucial challenge and opportunity for HCI in advancing global efforts toward data equity.