arXiv subjects

Kwangyoung Lee

Publications and source records attributed to Kwangyoung Lee.

2 recordsLinked to original sources

arXiv2026

Constructing Everyday Well-Being: Insights from God-Saeng for Personal Informatics

While Personal Informatics (PI) systems support behavior change, everyday well-being involves more than achieving individual target behaviors. It is shaped by cultural narratives that give actions meaning. In South Korea, the God-Saeng phenomenon, encompassing disciplined, collective, and publicly documented self-improvement practices, offers a lens into how well-being is negotiated in daily life. We conducted a 10-day probe (N=24) with bite-sized missions to examine how young adults engaged in God-Saeng. Participants relied on planning practices, accountability infrastructures, and datafication to stabilize themselves, yet these same routines also intensified pressures toward self-monitoring and performance. They navigated tensions between consistency and flexibility, authenticity and visibility, and productivity and broader values such as relationships, and reinterpreted ordinary activities through sociocultural contexts. These insights suggest design opportunities for PI systems that move beyond tracking, toward digital instruments that help users negotiate tensions, make meaning, and reflexively understand how technologies participate in their culturally and existentially situated well-being.

cs.HC↗

arXiv2025

AssurAI: Experience with Constructing Korean Socio-cultural Datasets to Discover Potential Risks of Generative AI

The rapid evolution of generative AI necessitates robust safety evaluations. However, current safety datasets are predominantly English-centric, failing to capture specific risks in non-English, socio-cultural contexts such as Korean, and are often limited to the text modality. To address this gap, we introduce AssurAI, a new quality-controlled Korean multimodal dataset for evaluating the safety of generative AI. First, we define a taxonomy of 35 distinct AI risk factors, adapted from established frameworks by a multidisciplinary expert group to cover both universal harms and relevance to the Korean socio-cultural context. Second, leveraging this taxonomy, we construct and release AssurAI, a large-scale Korean multimodal dataset comprising 11,480 instances across text, image, video, and audio. Third, we apply the rigorous quality control process used to ensure data integrity, featuring a two-phase construction (i.e., expert-led seeding and crowdsourced scaling), triple independent annotation, and an iterative expert red-teaming loop. Our pilot study validates AssurAI's effectiveness in assessing the safety of recent LLMs. We release AssurAI to the public to facilitate the development of safer and more reliable generative AI systems for the Korean community.

cs.AI↗