相关论文: SAGE: A Context-Aware Approach for Mining Privacy …
As Large Language Models are rapidly deployed across diverse applications from healthcare to financial advice, safety evaluation struggles to keep pace. Current benchmarks focus on single-turn interactions with generic policies, failing to…
With the increasing proliferation of mobile applications in our everyday experiences, the concerns surrounding ethics have surged significantly. Users generally communicate their feedback, report issues, and suggest new functionalities in…
Clinical NLP tasks such as mental health assessment from text, must take social constraints into account - the performance maximization must be constrained by the utmost importance of guaranteeing privacy of user data. Consumer protection…
Context: As mobile applications (Apps) widely spread over our society and life, various personal information is constantly demanded by Apps in exchange for more intelligent and customized functionality. An increasing number of users are…
As large language models (LLMs) achieve strong performance on traditional benchmarks, there is an urgent need for more challenging evaluation frameworks that probe deeper aspects of semantic understanding. We introduce SAGE (Semantic…
An increasing number of mental health services are offered through mobile systems, a paradigm called mHealth. Although there is an unprecedented growth in the adoption of mHealth systems, partly due to the COVID-19 pandemic, concerns about…
Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for large-scale, labeled, mental health-related datasets of users…
Background: User reviews of Telehealth and Patient Portal mobile applications (apps) hereon referred to as electronic health (eHealth) apps are a rich source of unsolicited patient feedback, revealing critical insights into patient…
The proliferation of LLM-based agents has led to increasing deployment of inter-agent collaboration for tasks like scheduling, negotiation, resource allocation etc. In such systems, privacy is critical, as agents often access proprietary…
Therapy and life-coaching apps have been rapidly growing in number, flavors, and popularity. However, their users routinely share highly sensitive and personal information, such as traumas, fantasies, desires, relationship difficulties, and…
Mental health is an extremely important subject, especially in these unprecedented times of the COVID-19 pandemic. Ubiquitous mobile phones can equip users to supplement psychiatric treatment and manage their mental health. Mobile Mental…
Retrieval-augmented generation (RAG) enhances the outputs of language models by integrating relevant information retrieved from external knowledge sources. However, when the retrieval process involves private data, RAG systems may face…
The rapid growth of AI-driven mental health mobile apps has raised concerns about their ethical considerations and user trust. This study proposed a natural language processing (NLP)-based framework to evaluate ethical aspects from…
Mental health disorders create profound personal and societal burdens, yet conventional diagnostics are resource-intensive and limit accessibility. Advances in artificial intelligence, particularly natural language processing and multimodal…
The widespread use of social media applications has raised significant privacy concerns, often highlighted in user reviews. These reviews also provide developers with valuable insights into improving apps by addressing issues and…
With the increasing proliferation of mobile applications in our daily lives, the concerns surrounding ethics have surged significantly. Users communicate their feedback in app reviews, frequently emphasizing ethical concerns, such as…
Mental health issues significantly impact individuals' daily lives, yet many do not receive the help they need even with available online resources. This study aims to provide accessible, stigma-free, personalized, and real-time mental…
Social media data has become a vital resource for studying mental health, offering real-time insights into thoughts, emotions, and behaviors that traditional methods often miss. Progress in this area has been facilitated by benchmark…
Amid growing global mental health concerns, particularly among vulnerable groups, natural language processing offers a tremendous potential for early detection and intervention of people's mental disorders via analyzing their postings and…
Sleep is vital for health, yet access to data alone does not guarantee improvement. While wearables and health apps enable tracking, users face a "Data-Action Gap," struggling to interpret metrics and translate them into action. Current…