2 citations · 4 across the 3 of their papers we have counts for
6 papers
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 decision-making but reduces trust
Shiyang Lai, Junsol Kim, Nadav Kunievsky +2
Current AI systems minimize risk by enforcing ideological neutrality, yet this may introduce automation bias by suppressing cognitive engagement in human decision-making. We conduc…
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…
Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voters
Yujin Potter, Shiyang Lai, Junsol Kim +2
How could LLMs influence our democracy? We investigate LLMs' political leanings and the potential influence of LLMs on voters by conducting multiple experiments in a U.S. president…