4 papers
AssurAI: Experience with Constructing Korean Socio-cultural Datasets to Discover Potential Risks of Generative AI
Chae-Gyun Lim, Seung-Ho Han, EunYoung Byun +51
The rapid evolution of generative AI necessitates robust safety evaluations. However, current safety datasets are predominantly English-centric, failing to capture specific risks i…
Unifying Inductive, Cross-Domain, and Multimodal Learning for Robust and Generalizable Recommendation
Chanyoung Chung, Kyeongryul Lee, Sunbin Park +1
Recommender systems have long been built upon the modeling of interactions between users and items, while recent studies have sought to broaden this paradigm by generalizing to new…
SAIF: A Comprehensive Framework for Evaluating the Risks of Generative AI in the Public Sector
Kyeongryul Lee, Heehyeon Kim, Joyce Jiyoung Whang
The rapid adoption of generative AI in the public sector, encompassing diverse applications ranging from automated public assistance to welfare services and immigration processes,…
Unveiling the Threat of Fraud Gangs to Graph Neural Networks: Multi-Target Graph Injection Attacks Against GNN-Based Fraud Detectors
Jinhyeok Choi, Heehyeon Kim, Joyce Jiyoung Whang
Graph neural networks (GNNs) have emerged as an effective tool for fraud detection, identifying fraudulent users, and uncovering malicious behaviors. However, attacks against GNN-b…