arXiv subjects

Ece Gumusel

Publications and source records attributed to Ece Gumusel.

8 recordsLinked to original sources

arXiv2026

AI Healthcare Chatbots as Information Infrastructure: A Large-Scale Study of User-Reported Breakdowns

AI healthcare chatbots are increasingly used to support health information seeking and self-management, yet their performance and impact on users remains to be studied. This study examines over 15,000 user reviews from 59 AI healthcare chatbot apps to explore how these systems function in everyday informational and emotional contexts. Topic modeling and interpretive analysis identify three recurring breakdowns: access barriers and service unreliability, user experience and interaction quality, and billing and customer support issues. Privacy and security concerns are associated with the most negative experiences. By framing AI healthcare chatbots as information infrastructures, our findings highlight how failures in access, usability, and trust affect users, offering actionable insights for designers, policymakers, and information professionals aiming to improve digital health systems.

cs.HC↗

arXiv2025

User Negotiations of Authenticity, Ownership, and Governance on AI-Generated Video Platforms: Evidence from Sora

As AI-generated video platforms rapidly advance, ethical challenges such as copyright infringement emerge. This study examines how users make sense of AI-generated videos on OpenAI's Sora by conducting a qualitative content analysis of user comments. Through a thematic analysis, we identified four dynamics that characterize how users negotiate authenticity, authorship, and platform governance on Sora. First, users acted as critical evaluators of realism, assessing micro-details such as lighting, shadows, fluid motion, and physics to judge whether AI-generated scenes could plausibly exist. Second, users increasingly shifted from passive viewers to active creators, expressing curiosity about prompts, techniques, and creative processes. Text prompts were perceived as intellectual property, generating concerns about plagiarism and remixing norms. Third, users reported blurred boundaries between real and synthetic media, worried about misinformation, and even questioned the authenticity of other commenters, suspecting bot-generated engagement. Fourth, users contested platform governance: some perceived moderation as inconsistent or opaque, while others shared tactics for evading prompt censorship through misspellings, alternative phrasing, emojis, or other languages. Despite this, many users also enforced ethical norms by discouraging the misuse of real people's images or disrespectful content. Together, these patterns highlighted how AI-mediated platforms complicate notions of reality, creativity, and rule-making in emerging digital ecosystems. Based on the findings, we discuss governance challenges in Sora and how user negotiations inform future platform governance.

cs.HC↗

arXiv2025

Can I Trust This Chatbot? Assessing User Privacy in AI-Healthcare Chatbot Applications

As Conversational Artificial Intelligence (AI) becomes more integrated into everyday life, AI-powered chatbot mobile applications are increasingly adopted across industries, particularly in the healthcare domain. These chatbots offer accessible and 24/7 support, yet their collection and processing of sensitive health data present critical privacy concerns. While prior research has examined chatbot security, privacy issues specific to AI healthcare chatbots have received limited attention. Our study evaluates the privacy practices of 12 widely downloaded AI healthcare chatbot apps available on the App Store and Google Play in the United States. We conducted a three-step assessment analyzing: (1) privacy settings during sign-up, (2) in-app privacy controls, and (3) the content of privacy policies. The analysis identified significant gaps in user data protection. Our findings reveal that half of the examined apps did not present a privacy policy during sign up, and only two provided an option to disable data sharing at that stage. The majority of apps' privacy policies failed to address data protection measures. Moreover, users had minimal control over their personal data. The study provides key insights for information science researchers, developers, and policymakers to improve privacy protections in AI healthcare chatbot apps.

cs.HC↗

arXiv2025

Multiverse Privacy Theory for Contextual Risks in Complex User-AI Interactions

In an era of increasing interaction with artificial intelligence (AI), users face evolving privacy decisions shaped by complex, uncertain factors. This paper introduces Multiverse Privacy Theory, a novel framework in which each privacy decision spawns a parallel universe, representing a distinct potential outcome based on user choices over time. By simulating these universes, this theory provides a foundation for understanding privacy through the lens of contextual integrity, evolving preferences, and probabilistic decision-making. Future work will explore its application using real-world, scenario-based survey data.

cs.CR↗

arXiv2024

Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning

The integration of generative Large Language Models (LLMs) into various applications, including the legal domain, has been accelerated by their expansive and versatile nature. However, when facing a legal case, users without a legal background often struggle to formulate professional queries and may inadvertently overlook critical legal factors when presenting their case narrative to LLMs. To address this issue, we propose the Diagnostic Legal Large Language Model (D3LM), which utilizes adaptive lawyer-like diagnostic questions to collect additional case information and then provides high-quality feedback. D3LM incorporates an innovative graph-based Positive-Unlabeled Reinforcement Learning (PURL) algorithm, enabling the generation of critical questions and enhancing user-LLM interactions. Moreover, an integrated LLM-based stopping criterion facilitates precise Court Views Generation (CVG). Our research also introduces a new English-language CVG dataset based on the US case law database, enriching the realm of LLM research and deployment with a vital dimension. D3LM surpasses classical LLMs by delivering outstanding performance and a remarkable user experience in the legal domain.

cs.CL↗

arXiv2024

"Sora is Incredible and Scary": Emerging Governance Challenges of Text-to-Video Generative AI Models

Text-to-video generative AI models such as Sora OpenAI have the potential to disrupt multiple industries. In this paper, we report a qualitative social media analysis aiming to uncover people's perceived impact of and concerns about Sora's integration. We collected and analyzed comments (N=292) under popular posts about Sora-generated videos, comparison between Sora videos and Midjourney images, and artists' complaints about copyright infringement by Generative AI. We found that people were most concerned about Sora's impact on content creation-related industries. Emerging governance challenges included the for-profit nature of OpenAI, the blurred boundaries between real and fake content, human autonomy, data privacy, copyright issues, and environmental impact. Potential regulatory solutions proposed by people included law-enforced labeling of AI content and AI literacy education for the public. Based on the findings, we discuss the importance of gauging people's tech perceptions early and propose policy recommendations to regulate Sora before its public release.

cs.CY↗

arXiv2024

User Privacy Harms and Risks in Conversational AI: A Proposed Framework

This study presents a unique framework that applies and extends Solove (2006)'s taxonomy to address privacy concerns in interactions with text-based AI chatbots. As chatbot prevalence grows, concerns about user privacy have heightened. While existing literature highlights design elements compromising privacy, a comprehensive framework is lacking. Through semi-structured interviews with 13 participants interacting with two AI chatbots, this study identifies 9 privacy harms and 9 privacy risks in text-based interactions. Using a grounded theory approach for interview and chatlog analysis, the framework examines privacy implications at various interaction stages. The aim is to offer developers, policymakers, and researchers a tool for responsible and secure implementation of conversational AI, filling the existing gap in addressing privacy issues associated with text-based AI chatbots.

cs.HC↗

arXiv2024

"The teachers are confused as well": A Multiple-Stakeholder Ethics Discussion on Large Language Models in Computing Education

Large Language Models (LLMs) are advancing quickly and impacting people's lives for better or worse. In higher education, concerns have emerged such as students' misuse of LLMs and degraded education outcomes. To unpack the ethical concerns of LLMs for higher education, we conducted a case study consisting of stakeholder interviews (n=20) in higher education computer science. We found that students use several distinct mental models to interact with LLMs - LLMs serve as a tool for (a) writing, (b) coding, and (c) information retrieval, which differ somewhat in ethical considerations. Students and teachers brought up ethical issues that directly impact them, such as inaccurate LLM responses, hallucinations, biases, privacy leakage, and academic integrity issues. Participants emphasized the necessity of guidance and rules for the use of LLMs in higher education, including teaching digital literacy, rethinking education, and having cautious and contextual policies. We reflect on the ethical challenges and propose solutions.

cs.CY↗