With the development of consumer surveillance technologies, monitoring has become increasingly accessible and woven into family life. Prior work has examined parents’ attitudes, privacy concerns, and selected uses of surveillance technologies like smart cameras and location-tracking apps, but offers limited accounts of how parents, as surveillants, configure and experience these technologies themselves in daily parenting. We address this gap by focusing on baby monitoring technologies (BMTs) as a high-salience context during a sensitive stage of family life. Using inductive thematic analysis of Reddit discussions, we examine how parents engage with BMTs in practice. Our findings revealed how parents actively assemble and configure BMTs, navigate and manage their emotions through them, negotiate privacy frictions and boundaries, and safeguard security in their use of such technologies within parenting and caregiving. We conclude by discussing implications for surveillance research and design for monitoring technologies in care.

Gaming is a meaningful part of children’s lives, yet its safety has drawn increasing concerns from scholars and the public. On platforms like Roblox, children may encounter extremist roleplay, scams, or virtual rape. Prior research has emphasized technical interventions that address risks after they occur and ethical frameworks for game design, but children’s perspectives on safety design remain missing. To address this gap, we conducted a cooperative inquiry study to co-design safety mechanisms with 22 children aged 7–12. Children proposed designs emphasizing transparent information about games and purchases, community accountability through reporting and reviews, player empowerment to manage social boundaries and engagement, and age-appropriate game navigation. Our findings extend safety-by-design research by foregrounding children’s perspectives, showing how they envision safety mechanisms across both game and platform design, while enjoying safe play through risk exposure, allocating trust, and balancing platform support with agency.

Abstract The smooth functioning of the gig economy relies on gig workers’ local execution of tasks assigned by global platforms like Uber. Within this broader workforce, gig drivers (e.g., rideshare and delivery workers) draw on their local knowledge – information and skills specific to a particular region – to navigate and optimize routes. Building on a substantial body of research on gig work, this study turns attention to how gig drivers develop and deploy knowledge practices within the gig economy infrastructure. To address this, we analyzed 25 semi-structured interviews, revealing how drivers’ knowledge practices enable them to adapt platform systems to local conditions, develop algorithmic-local knowledge by integrating insights about platform behavior and market dynamics, and strategically regulate knowledge exchange with peers. We discuss how such knowledge practices play a critical role in localizing platform work, bridging the gaps between gig platforms’ global standards and local conditions.

Computer-supported cooperative work (CSCW) researchers have studied how teenagers experience safety risks such as cyberbullying and scam, but little attention has been paid to gambling. While teenagers’ access to traditional gambling, such as casinos, has been tightly regulated, internet technology has given rise to emergent forms of online gambling, which increasingly permeate teenagers’ everyday lives and exposes them to safety threats and gambling-related harm. Addressing this issue requires a deeper understanding of teenagers' risk perception of gambling, as it not only shapes individual decision-making but also influences societal behaviors and informs policies aimed at mitigating its harmful effects. To understand how teenagers perceive and deal with gambling risks, we analyzed how teenagers engaged in collective sensemaking of gambling in the r/teenagers subreddit, one of the largest online communities on Reddit for teenagers. Our findings revealed various gambling risks perceived by teenagers, the strategies they adopt to mitigate them, and the strong sense of agency they demonstrate in engaging with gambling-related content. Based on these findings, we propose gambling intervention recommendations targeting teenagers, as well as implications for platforms and policymaking.

The HCI community has been actively developing and studying the use of Personal Informatics (PI) systems.While celebrating the headways, researchers have uncovered many unintended consequences of using PI systems, such as data-induced stress and obsessive tracking, but there has been a lack of systematic analysis of these consequences and their underlying causes.In this work, we reviewed 172 PI research articles, highlighting that tracking and interacting with personal data can adversely affect individuals' cognitive load, emotional well-being, social acts, and behaviors, while bringing practical challenges.By synthesizing the pathways through which these consequences occur, we recognized issues in the data-centric design ideology, variations across tracking needs and literacy, the evolving social dynamics, and individuals' intention-behavior gap.Reflecting on the findings, we discuss how to best leverage personal data in our lives and propose a practice-oriented research agenda to mitigate these unintended consequences.

Personally identifiable information (PII) is a fundamental concept in privacy research and regulations. Understanding users' perspectives on PII is critical, as their understanding of PII can significantly affect their privacy decisions and practices. While much research has explored users’ privacy perceptions and disclosure preferences regarding PII, less attention has been focused on how users internally define and conceptualize PII. In this study, we conducted interviews with 32 participants to investigate their conceptualization and understanding of PII, using period and fertility tracking apps as the context. Our findings reveal how users perceive the processes and contexts through which personal information, by becoming identifiable, transitions into PII, as well as concerns about data sharing and misuse in these apps. We conclude by advocating for addressing the misalignment between users' perceptions of PII and the regulatory protections and privacy designs surrounding it.

Tracking is inherent in and central to the gig economy. Platforms track gig workers’ performance through metrics such as acceptance rate and punctuality, while gig workers themselves engage in self-tracking. Although prior research has extensively examined how gig platforms track workers through metrics – with some studies briefly acknowledging the phenomenon of self-tracking among workers – there is a dearth of studies that explore how and why gig workers track themselves. To address this, we conducted 25 semi-structured interviews, revealing how gig workers self-track to manage accountabilities to themselves and external entities across three identities: the holistic self, the entrepreneurial self, and the platformized self. We connect our findings to neoliberalism, through which we contextualize gig workers’ self-accountability and the invisible labor of self-tracking. We further discuss how self-tracking mitigates information and power asymmetries in gig work and offer design implications to support gig workers’ multi-dimensional self-tracking.

After the overturn of Roe v. Wade gave states the license to ban abortion, numerous people in US have grown to worry about privacy in using period and fertility tracking apps. To address these concerns, some app companies have issued public statements to engage in privacy communication with their users. Prior literature has investigated period and fertility tracking apps’ data practices in their privacy policies. However, there remains a dearth of knowledge regarding how companies use privacy communication to address historic privacy-related events such as the overturn. To address the gap, this study investigated app companies’ public statements addressing the overturn of Roe using a combined approach of thematic and discourse analysis. Our findings revealed that companies strategically emphasize their commitment to privacy by demonstrating how their business practices and values are closely intertwined with their efforts to protect user data. We conclude by discussing translatable implications for privacy research.

On June 24, 2022, the U.S. Supreme Court overturned Roe v. Wade, which has led to full bans on most abortions in 14 states within one year. Many people in the U.S. use period and fertility tracking apps for reproductive healthcare and concerns have arisen about the privacy risks these apps might pose in the wake of Roe reversal. Existing literature on privacy risks of period and fertility tracking apps has primarily examined the privacy policies and practices of these apps. However, how users make sense of the privacy risks of these apps, especially in the post-Roe time, remains understudied. This study explores collective privacy sensemaking on social media, a practice in which people collectively make sense of a privacy situation. Our findings reveal how people contextualize privacy issues, speculate about the associated risks, as well as explore risk mitigation strategies. We conclude with privacy design implications for privacy design in period and fertility tracking apps and contribute insights that could inform policymaking and legal perspectives.

Automated machine learning (AutoML) is envisioned to make ML techniques accessible to ordinary users. Recent work has investigated the role of humans in enhancing AutoML functionality throughout a standard ML workflow. However, it is also critical to understand how users adopt existing AutoML solutions in complex, real-world settings from a holistic perspective. To fill this gap, this study conducted semi-structured interviews of AutoML users (N = 19) focusing on understanding (1) the limitations of AutoML encountered by users in their real-world practices, (2) the strategies users adopt to cope with such limitations, and (3) how the limitations and workarounds impact their use of AutoML. Our findings reveal that users actively exercise user agency to overcome three major challenges arising from customizability, transparency, and privacy. Furthermore, users make cautious decisions about whether and how to apply AutoML on a case-by-case basis. Finally, we derive design implications for developing future AutoML solutions.

Search engine results can include misinformation that is inaccurate, misleading, or even harmful. But people may not recognize or realize false information results when searching online. We suspect that the percentage of misinformation search results (misinformation density) may influence people’s search activities, learning outcomes, and search experience. We conducted a zoom-mediated “lab” user study to examine this matter. The experiment used a between-subjects design. We asked 60 participants to finish two health information search tasks using search engines with High, Medium, or Low misinformation density levels. To create these experimental settings, we trained task-dependent text classifiers to manipulate the number of correct and misinformation results displayed on SERPs. We collected participants’ search activities, responses to pre-task and post-task surveys, and answers to task-related factual questions before and after searching.