2 citations · 9 across the 11 of their papers we have counts for
5 papers · 1 filter
VMDT: Decoding the Trustworthiness of Video Foundation Models
Yujin Potter, Zhun Wang, Nicholas Crispino +11
As foundation models become more sophisticated, ensuring their trustworthiness becomes increasingly critical; yet, unlike text and image, the video modality still lacks comprehensi…
Biased AI improves human performance but reduces perceived helpfulness
Shiyang Lai, Jiwoong Choi, Junsol Kim +3
Artificial intelligence (AI) increasingly shapes how people think, engage, and evaluate information. To minimize risk, most current systems are designed to present as ideologically…
Frontier AI's Impact on the Cybersecurity Landscape
Yujin Potter, Wenbo Guo, Zhun Wang +6
The impact of frontier AI (i.e., AI agents and foundation models) in cybersecurity is rapidly increasing. In this paper, we comprehensively analyze this trend through multiple aspe…
MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
Chejian Xu, Jiawei Zhang, Zhaorun Chen +22
Multimodal foundation models (MMFMs) play a crucial role in various applications, including autonomous driving, healthcare, and virtual assistants. However, several studies have re…
Political Neutrality in AI Is Impossible- But Here Is How to Approximate It
Jillian Fisher, Ruth E. Appel, Chan Young Park +9
AI systems often exhibit political bias, influencing users' opinions and decisions. While political neutrality-defined as the absence of bias-is often seen as an ideal solution for…