AI companions are increasingly popular among teenagers, yet current platforms lack safeguards to address developmental risks and harmful normalization. Despite growing concerns, little is known about how parents and developmental psychology experts assess these interactions or what protections they consider necessary. We conducted 26 semi-structured interviews with parents and experts, who reviewed real-world youth–AI companion conversation snippets. We found that stakeholders assessed risks contextually, attending to factors such as youth maturity, AI character age, and how AI characters modeled values and norms. We also identified distinct logics of assessment: parents flagged single events, such as a mention of suicide or flirtation, as high risk, whereas experts looked for patterns over time, such as repeated references to self-harm or sustained dependence. Both groups proposed interventions, with parents favoring broader oversight and experts preferring cautious, crisis-only escalation paired with youth-facing safeguards. These findings provide directions for embedding safety into AI companion design.

Figure 1: From left to right: (a) the original website, (b) Option 1 (Regenerated HTML), and (c) Option 2 (Reorganized HTML Tags).Option 1 is generated using a GenAI-powered extension that rewrites the HTML to enhance accessibility, while Option 2 reorganizes the original HTML tags to address accessibility issues without altering the visual design.This example highlights the changes made in both versions compared to the original website.Both versions simplify navigation by removing single-category headings, reducing clutter and minimizing fatigue for screen reader users.

Large Language Models (LLMs) are increasingly used by teenagers and young adults in everyday life, ranging from emotional support and creative expression to educational assistance. However, their unique vulnerabilities and risk profiles remain under-examined in current safety benchmarks and moderation systems, leaving this population disproportionately exposed to harm. In this work, we present Youth AI Risk (YAIR), the first benchmark dataset designed to evaluate and improve the safety of youth–LLM interactions. YAIR consists of 12,449 annotated conversation snippets spanning 78 fine-grained risk types, grounded in a taxonomy of youth-specific harms such as grooming, boundary violation, identity confusion, and emotional overreliance. We systematically evaluate widely adopted moderation models on YAIR and find that existing approaches substantially underperform in detecting youth-centered risks, often missing contextually subtle yet developmentally harmful interactions. To address these gaps, we introduce YouthSafe, a real-time risk detection model optimized for youth–GenAI contexts. YouthSafe significantly outperforms prior systems across multiple metrics on risk detection and classification, offering a concrete step toward safer and more developmentally appropriate AI interactions for young users.

Stablecoins, a type of cryptocurrency pegged to another asset to maintain a stable price, have become an important part of the cryptocurrency ecosystem. Prior studies have primarily focused on examining the security of stablecoins from technical and theoretical perspectives, with limited investigation into users' risk perceptions and security behaviors in stablecoin practices. To address this research gap, we conducted a mixed-method study that included constructing a stablecoin interaction framework based on the literature, which informed the design of our interview protocol, semi-structured interviews (n=21), and Reddit data analysis (9,326 posts). We found that participants see stable value and regulatory compliance as key security advantages of stablecoins over other cryptocurrencies. However, participants also raised concerns about centralization risks in fiat-backed stablecoins, perceived challenges in crypto-backed stablecoins due to limited reliance on fully automated execution, and confusion regarding the complex mechanisms of algorithmic stablecoins. We proposed improving user education and optimizing mechanisms to address these concerns and promote the safer use of stablecoins.

The widespread use of Generative Artificial Intelligence (GAI) among teenagers has led to significant misuse and safety concerns. To identify risks and understand parental controls challenges, we conducted a content analysis on Reddit and interviewed 20 participants (seven teenagers and 13 parents). Our study reveals a significant gap in parental awareness of the extensive ways children use GAI, such as interacting with character-based chatbots for emotional support or engaging in virtual relationships. Parents and children report differing perceptions of risks associated with GAI. Parents primarily express concerns about data collection, misinformation, and exposure to inappropriate content. In contrast, teenagers are more concerned about becoming addicted to virtual relationships with GAI, the potential misuse of GAI to spread harmful content in social groups, and the invasion of privacy due to unauthorized use of their personal data in GAI applications. The absence of parental control features on GAI platforms forces parents to rely on system-built controls, manually check histories, share accounts, and engage in active mediation. Despite these efforts, parents struggle to grasp the full spectrum of GAI-related risks and to perform effective real-time monitoring, mediation, and education. We provide design recommendations to improve parent-child communication and enhance the safety of GAI use.

Social media platforms are an effective channel for businesses to reach potential audiences through targeted advertising. As the user base of these platforms expands and diversifies, research on targeted advertising and social media needs to go beyond well-studied Western contexts. In an online survey (n=412), we compared users' privacy-related perceptions and behaviors regarding targeted ads on social media in the United States (as a baseline representing Western contexts) and three South Asian countries: Bangladesh, India, and Pakistan. We found that participants in the US perceived significantly fewer benefits and more concerns related to security and privacy about targeted ads than those in the three South Asian countries. We also identified that individual's cultural values and religious affiliations influenced the observed cross-country variances. For instance, US participants identified less with vertical collectivism and vertical individualism than South Asian participants; these two cultural dimensions were, in turn, positively associated with perceived benefits. Our findings highlight the limitation of using one's country as a proxy for culture, as our findings show users' privacy perceptions regarding targeted advertising on social media are more fundamentally associated with their cultural values and religion. We discuss the corresponding design, education, and regulatory implications for targeted advertising on social media.

Cryptocurrency wallets come in various forms, each with unique usability and security features. Through interviews with 24 users, we explore reasons for selecting wallets in different contexts. Participants opt for smart contract wallets to simplify key management, leveraging social interactions. However, they prefer personal devices over individuals as guardians to avoid social cybersecurity concerns in managing guardian relationships. When engaging in high-stakes or complex transactions, they often choose browser-based wallets, leveraging third-party security extensions. For simpler transactions, they prefer the convenience of mobile wallets. Many participants avoid hardware wallets due to usability issues and security concerns with respect to key recovery service provided by manufacturer and phishing attacks. Social networks play a dual role: participants seek security advice from friends, but also express security concerns in soliciting this help. We offer novel insights into how and why users adopt specific wallets. We also discuss design recommendations for future wallet technologies based on our findings.

While people’s perceptions of targeted ads have been studied extensively from a Western perspective (e.g., North America, Europe), we know little about users’ perceptions in the South Asian region. We interviewed 40 participants from two South Asian countries, Bangladesh and India, to explore their perceptions and practices regarding targeted ads on social media platforms. Participants identified emerging ad types, such as influencer-based ads and soft ads, through articles. In addition, participants often outweighed discounts over product quality when viewing ads. We also observed novel user mental models of targeted ads based on mobile app permissions and excessive AI usage. Participants often preferred ad control over transparency. While most participants rarely used ad settings, some controlled ads by changing mobile app permissions or muting ads on social media platforms. Participants also raised concerns about fraudulent targeted ads and privacy violations due to device sharing. We present potential design ideas to mitigate these concerns.

Due to the challenges to detect and filter phishing emails, it is inevitable that some phishing emails can still reach a user’s inbox. As a result, email providers such as Gmail have implemented phishing warnings to help users to better recognize phishing attempts. Existing research has primarily focused on phishing warnings for sighted users and yet it is not well understood how people with visual impairments interact with phishing emails and warnings. In this paper, we worked with a group of users (N=41) with visual impairments to study the effectiveness of existing warnings and explore more inclusive designs (using Gmail warning designs as a baseline for comparison). We took a multipronged approach including an exploratory study (to understand the challenges faced by users), user-in-the-loop design and prototyping, and the main study (to assess the impact of design choices). Our results show that users with visual impairments often miss existing Gmail warnings because the current design (e.g., warning position, HTML tags used) does not match well with screen reader users’ reading habits. The inconsistencies of the warnings (e.g., across the Standard and HTML view) also create obstacles to users. We show that an inclusive design (combining audio warning, shortcut key, and warning page overlay) can effectively increase the warning noticeability. Based on our results, we make a number of recommendations to email providers.