activity
20242026
collaborators

7 papers

cs.HC2026

Child Safety in Generative AI: An Expert-Guided and Incident-Grounded Evaluation Framework

Haein Kong

As generative AI is increasingly used by children and adolescents, there is a growing need for risk evaluation frameworks that account for child-specific harms. However, most exist…

cs.CV2026

FCC: Fully Connected Correlation for One-Shot Segmentation

Seonghyeon Moon, Haein Kong, Muhammad Haris Khan +2

Few-shot segmentation (FSS) aims to segment the target object in a query image using only a small set of support images and masks. Therefore, having strong prior information for th…

cs.CV2025

Judging from Support-set: A New Way to Utilize Few-Shot Segmentation for Segmentation Refinement Process

Seonghyeon Moon, Qingze, Liu +2

Segmentation refinement aims to enhance the initial coarse masks generated by segmentation algorithms. The refined masks are expected to capture more details and better contours of…

cs.CY2025

Persuasion and Safety in the Era of Generative AI

Haein Kong

As large language models (LLMs) achieve advanced persuasive capabilities, concerns about their potential risks have grown. The EU AI Act prohibits AI systems that use manipulative…

cs.CY2025

Examining Racial Stereotypes in YouTube Autocomplete Suggestions

Eunbin Ha, Haein Kong, Shagun Jhaver

Autocomplete is a popular search feature that predicts queries based on user input and guides users to a set of potentially relevant suggestions. In this study, we examine what You…

cs.CL2025

When LLM Therapists Become Salespeople: Evaluating Large Language Models for Ethical Motivational Interviewing

Haein Kong, Seonghyeon Moon

Large language models (LLMs) have been actively applied in the mental health field. Recent research shows the promise of LLMs in applying psychotherapy, especially motivational int…