arXiv subjects

Jiaxun Cao

Publications and source records attributed to Jiaxun Cao.

7 recordsLinked to original sources

arXiv2026

The Impact of Security and Privacy Controls on Users' Emotional Engagement with Generative AI Chatbots

Chatbots powered by generative AI (e.g., OpenAI's ChatGPT and Google's Gemini) are increasingly being appropriated for emotional support and companionship. These tools offer a suite of security and privacy (S&P) controls, including model training opt-outs and memory toggles, yet how the presence of these controls influences users' attitudes toward emotionally sensitive disclosure remains understudied. We conducted a mixed-methods vignette study with 354 U.S. participants to examine how S&P controls influence users' willingness to engage with generative AI chatbots for emotional support, their perceptions of how protected they are when using these systems, and their perceptions of how effective the chatbots are for providing support. Controls enabling deletion of disclosures had the largest positive impact: these offerings outperformed technically sophisticated controls such as local-only processing and model training opt-outs, where participants expressed difficulty understanding the underlying mechanisms. Yet trust remains fragile, and participants often doubted S&P controls would function as promised. We conclude with actionable recommendations informed by our results to bridge users' comprehension gaps, build credible assurances, and properly calibrate barriers for users in distress.

cs.HC↗

arXiv2026

Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AI

Users increasingly rely on consumer-facing generative AI (GenAI) for tasks ranging from everyday needs to sensitive use cases. Yet, it remains unclear whether and how existing security and privacy (S&P) communications in GenAI tools shape users' adoption decisions and experiences. Understanding how users seek, interpret, and evaluate S&P information is critical for designing usable transparency that users can trust and act on. We conducted semi-structured interviews and design sessions with 21 U.S. GenAI users. Our findings suggest that available S&P information rarely drove initial adoption in practice, as participants often perceived it as incomplete, ineffective, or not credible. Instead, they relied on rough proxies (e.g., popularity) to infer S&P practices. After adoption, S&P uncertainty constrained participants' willingness to use GenAI tools, especially for high-stakes purposes, and, in some cases, contributed to discontinued use. Participants therefore called for transparency that supports decisions and actions through trustworthy information (e.g., independent evaluations) and usable interfaces (e.g., on-demand disclosure). We categorize participants' desired design practices into five dimensions to facilitate systematic future investigation into best practices. We conclude with recommendations for researchers, designers, and policymakers to improve S&P transparency in consumer-facing GenAI.

cs.HC↗

arXiv2025

Exploring User Security and Privacy Attitudes and Concerns Toward the Use of General-Purpose LLM Chatbots for Mental Health

Individuals are increasingly relying on large language model (LLM)-enabled conversational agents for emotional support. While prior research has examined privacy and security issues in chatbots specifically designed for mental health purposes, these chatbots are overwhelmingly "rule-based" offerings that do not leverage generative AI. Little empirical research currently measures users' privacy and security concerns, attitudes, and expectations when using general-purpose LLM-enabled chatbots to manage and improve mental health. Through 21 semi-structured interviews with U.S. participants, we identified critical misconceptions and a general lack of risk awareness. Participants conflated the human-like empathy exhibited by LLMs with human-like accountability and mistakenly believed that their interactions with these chatbots were safeguarded by the same regulations (e.g., HIPAA) as disclosures with a licensed therapist. We introduce the concept of "intangible vulnerability," where emotional or psychological disclosures are undervalued compared to more tangible forms of information (e.g., financial or location-based data). To address this, we propose recommendations to safeguard user mental health disclosures with general-purpose LLM-enabled chatbots more effectively.

cs.CY↗

arXiv2024

Understanding Parents' Perceptions and Practices Toward Children's Security and Privacy in Virtual Reality

Recent years have seen a sharp increase in the number of underage users in virtual reality (VR), where security and privacy (S\&P) risks such as data surveillance and self-disclosure in social interaction have been increasingly prominent. Prior work shows children largely rely on parents to mitigate S\&P risks in their technology use. Therefore, understanding parents' S\&P knowledge, perceptions, and practices is critical for identifying the gaps for parents, technology designers, and policymakers to enhance children's S\&P. While such empirical knowledge is substantial in other consumer technologies, it remains largely unknown in the context of VR. To address the gap, we conducted in-depth semi-structured interviews with 20 parents of children under the age of 18 who use VR at home. Our findings highlight parents generally lack S\&P awareness due to the perception that VR is still in its infancy. To protect their children's interactions with VR, parents currently primarily rely on active strategies such as verbal education about S\&P. Passive strategies such as using parental controls in VR are not commonly used among our interviewees, mainly due to their perceived technical constraints. Parents also highlight that a multi-stakeholder ecosystem must be established towards more S\&P support for children in VR. Based on the findings, we propose actionable S\&P recommendations for critical stakeholders, including parents, educators, VR companies, and governments.

cs.HC↗

arXiv2024

Understanding Young People's Creative Goals with Augmented Reality

Young people are major consumers of Augmented Reality (AR) tools like Pokémon GO, but they rarely engage in creating these experiences. Creating with technology gives young people a platform for expressing themselves and making social connections. However, we do not know what young people want to create with AR, as existing AR authoring tools are largely designed for adults. To investigate the requirements for an AR authoring tool, we ran eight design workshops with 17 young people in Argentina and the United States that centered on young people's perspectives and experiences. We identified four ways in which young people want to create with} AR, and contribute the following design implications for designers of AR authoring tools for young people: (1) Blending imagination into AR scenarios to preserve narratives, (2) Making traces of actions visible to foster social presence, (3) Exploring how AR artifacts can serve as invitations to connect with others, and (4) Leveraging information asymmetry to encourage learning about the physical world.

cs.HC↗

arXiv2023

"I'm Not Confident in Debiasing AI Systems Since I Know Too Little": Teaching AI Creators About Gender Bias Through Hands-on Tutorials

Gender bias is rampant in AI systems, causing bad user experience, injustices, and mental harm to women. School curricula fail to educate AI creators on this topic, leaving them unprepared to mitigate gender bias in AI. In this paper, we designed hands-on tutorials to raise AI creators' awareness of gender bias in AI and enhance their knowledge of sources of gender bias and debiasing techniques. The tutorials were evaluated with 18 AI creators, including AI researchers, AI industrial practitioners (i.e., developers and product managers), and students who had learned AI. Their improved awareness and knowledge demonstrated the effectiveness of our tutorials, which have the potential to complement the insufficient AI gender bias education in CS/AI courses. Based on the findings, we synthesize design implications and a rubric to guide future research, education, and design efforts.

cs.HC↗

arXiv2023

On the Mechanics of NFT Valuation: AI Ethics and Social Media

As CryptoPunks pioneers the innovation of non-fungible tokens (NFTs) in AI and art, the valuation mechanics of NFTs has become a trending topic. Earlier research identifies the impact of ethics and society on the price prediction of CryptoPunks. Since the booming year of the NFT market in 2021, the discussion of CryptoPunks has propagated on social media. Still, existing literature hasn't considered the social sentiment factors after the historical turning point on NFT valuation. In this paper, we study how sentiments in social media, together with gender and skin tone, contribute to NFT valuations by an empirical analysis of social media, blockchain, and crypto exchange data. We evidence social sentiments as a significant contributor to the price prediction of CryptoPunks. Furthermore, we document structure changes in the valuation mechanics before and after 2021. Although people's attitudes towards Cryptopunks are primarily positive, our findings reflect imbalances in transaction activities and pricing based on gender and skin tone. Our result is consistent and robust, controlling for the rarity of an NFT based on the set of human-readable attributes, including gender and skin tone. Our research contributes to the interdisciplinary study at the intersection of AI, Ethics, and Society, focusing on the ecosystem of decentralized AI or blockchain. We provide our data and code for replicability as open access on GitHub.

cs.CY↗