An increasing number of LLM-based applications are being developed to facilitate romantic relationships with AI partners, yet the safety and privacy risks in these partnerships remain largely underexplored. In this work, we investigate privacy in human–AI romantic relationships through an interview study (N=17), examining participants’ experiences and privacy perceptions across the three stages of exploration, intimacy, and dissolution, alongside an analysis of the platforms they used. We found that these relationships took varied forms, from one-to-one to one-to-many, and were shaped by multiple actors, including creators, platforms, and moderators. AI partners were perceived as having agency, actively negotiating privacy boundaries with participants and sometimes encouraging disclosure of personal details. As intimacy deepened, these boundaries became more permeable, though some participants expressed concerns such as conversation exposure and sought to preserve anonymity. Overall, AI platform affordances and diverse relational dynamics expand the privacy landscape, underscoring the need to rethink how privacy is constructed in human–AI romantic relationships.

Generative AI assistants are being rapidly adopted in universities, supporting students in coursework and faculty in academic tasks. To address privacy concerns, some institutions introduced institutional AI assistants, typically wrappers around commercial models (e.g., ChatGPT) with added governance and data protections. However, university-affiliated users appear to rely more on commercial tools (e.g., ChatGPT, Gemini). We conducted a survey (n=260) at one U.S. university to examine preferences, usage scenarios, and perceptions of trust, privacy, and experience with institutional and commercial AI. Participants trusted institutional tools more and considered them more privacy protective, nevertheless commercial tools were often favored for writing, programming, and learning due to their features and utility. Findings reveal a trade-off between privacy and trust versus utility, highlighting complementary adoption patterns and design opportunities for both institutional and commercial AI in higher education.

While web agents gained popularity by automating web interactions, their requirement for interface access introduces privacy risks that are understudied, particularly from users’ perspective. Through a formative study (N=15), we found that users frequently misunderstand agent data practices, and desire unobtrusive, transparent data management. To achieve this, we developed PrivWeb, a trusted add-on on web agents that utilizes a localized LLM to anonymize private information on interfaces based on user preferences. It employs a tiered delegation to balance automation and intrusiveness, using ambient notifications for low-sensitivity data and enforces a mandatory pause for high-sensitivity data. The user study (N=14) across travel, information retrieval, shopping, and entertainment tasks showed that PrivWeb enhances perceived privacy protection and trust compared to transparency-only baselines, without increasing cognitive load. Crucially, we identified user delegation strategies: they prefer to manually execute sensitive steps for high-sensitivity data, while granting agent access to low-sensitivity data.

As AI-generated health information proliferates online and becomes increasingly indistinguishable from human-sourced information, it becomes critical to understand how people trust and label such content, especially when the information is inaccurate. We conducted two complementary studies: (1) a mixed-methods survey (N=142) employing a 2 (source: Human vs. LLM) × 2 (label: Human vs. AI) × 3 (type: General, Symptom, Treatment) design, and (2) a within-subjects lab study (N=40) incorporating eye-tracking and physiological sensing (ECG, EDA, skin temperature). Participants were presented with health information varying by source-label combinations and asked to rate their trust, while their gaze behavior and physiological signals were recorded. We found that LLM-generated information was trusted more than human-generated content, whereas information labeled as human was trusted more than that labeled as AI. Trust remained consistent across information types. Eye-tracking and physiological responses varied significantly by source and label. Machine learning models trained on these behavioral and physiological features predicted binary self-reported trust levels with 73 % accuracy and information source with 65 % accuracy. Our findings demonstrate that adding transparency labels to online health information modulates trust. Behavioral and physiological features show potential to verify trust perceptions and indicate if additional transparency is needed.

The rapid growth of platforms for customizing Large Language Models (LLMs), such as OpenAI’s GPTs, has raised new privacy and security concerns, particularly related to the exposure of user data via third-party API integrations in LLM apps. To assess privacy risks and data practices, we conducted a large-scale analysis of OpenAI’s GPTs ecosystem. Through the analysis of 5,286 GPTs and the 44,102 parameters they use through API calls to external services, we systematically investigated the types of user data collected, as well as the completeness and discrepancies between actual data flows and GPTs’ stated privacy policies. Our results highlight that approximately 35% of API parameters enable the sharing of sensitive or personally identifiable information, yet only 15% of corresponding privacy policies provide complete disclosure. By quantifying these discrepancies, our study exposes critical privacy risks and underscores the need for stronger oversight and support tools in LLM-based application development. Furthermore, we uncover widespread problematic practices among GPT creators, such as missing or inaccurate privacy policies and a misunderstanding of their privacy responsibilities. Building on these insights, we propose design recommendations that include actionable measurements to improve transparency and informed consent, enhance creator responsibility, and strengthen regulation.

GPTs are customized LLM apps built on OpenAI's large language model.Any individual or organization can use and create GPTs without needing programming skills.However, the rapid proliferation of over three million GPTs has raised signifcant privacy concerns.To explore the privacy perspectives of users and creators, we interviewed 23 GPT users with varying levels of creation experience.Our fndings reveal blurred lines between user and creator roles and their understanding of GPT data fows.Participants raised concerns about data handling during collection, processing, and dissemination, alongside the lack of privacy regulations.Creators also worried about loss of their proprietary knowledge.In response, participants adopted practices like self-censoring input, evaluating GPT actions, and minimizing usage traces.Focusing on the dual role of user-creators, we fnd that expertise and responsibility shape privacy perceptions.Based on these insights, we propose practical recommendations to improve data transparency and platform regulations.

People often use multiple communication apps concurrently for messaging, yet how they use these channels together and the motivations for switching between them remain underexplored. To investigate this, we conducted a two-week diary study followed by interviews (N=17), examining channel-switching practices and the management of social boundaries. Our findings reveal that users switch between multiple communication channels with the same contacts, driven by factors such as different topics, expectations for fast or slow communication rhythm, specific features suited to the situation, and app-specific vibes that match their mood at the moment. Additionally, we identify inertia, where users tend to stay on the app where the last interaction occurred, as the opposite of active channel switching. Our findings show that users do not actively distinguish apps based on relationships. Instead, their contacts are dispersed across different communication apps due to adaptive behaviors. These behaviors include users continually adapting to unique relationships, evolving communication needs, and app changes over time. Based on these insights, we propose design implications for multichannel communication to help users manage cross-app communication flows effectively.

Households increasingly play and engage with video games. We examined how households play video games among 20 interviewees coming from varied and familial households. Our study focused on interactions, examining how gaming influences daily household dynamics. Previous studies have focused mainly on the impact on relationships. Looking at households led us to observe fluid role dynamics around gaming. Our findings map the stages of how households play games from gaining plausible momentum, actions, conversations, and roles taken during game sessions, and reflections after gaming. Our findings highlight a novel role of the Gamer Host leading the game session and attending to everyone's enjoyment. Our observations exemplify the supportive and positive social outcomes close-knit gaming can afford and implications for achieving harmonious gaming in households. Our findings tie to prospects on communal and social aspects on technology use providing new perspectives on user experiences in an immediate social environment.

In this paper, we investigate browsing clutter, which refers to cluttered experiences of users due to buildup of disorganized browser elements and information. We studied what users experience as clutter, what behaviors and factors contribute to the clutter, and what users do when they experience clutter through an interview study (N = 16) and an online survey study (N = 400). Based on our studies, browsing clutter includes the amount of tabs and windows, content of the web pages and interactive elements, navigation, and search process. We identified sources of browsing clutter from task characteristics, such as importance and complexity, to user habits, such as multitasking and tab closing. To reveal the dynamics of browsing clutter, we modeled how browsing clutter is predicted by specific browsing habits and coping strategies. Our model demonstrates how individual forms of clutter are interrelated and altered by behavior. We discuss how browsing clutter relates to information overload.