Y. Kelly Wu

Email: kellywu at mail dot rit dot edu

I am a 5th-year Ph.D. student at Rochester Institute of Technology advised by Dr. Matthew Wright. I am currently a part of the DeFake Project. I held a B.S. degree in Mathematics and Economics from Georgetown University.

My research lies at the intersection of multimedia authenticity and Human-Computer Interaction (HCI), aiming to bridge technical detection methods with how people interpret, trust, and act on their outputs. I study explainability, terminology, and communication in media forensics, with a focus on ensuring that authenticity assessments are meaningful and usable in real-world contexts.

selected publications

Beyond the Naked Eye: Empirical Study of How People Perceive, Detect, and Respond to AI-Manipulated Videos

Kaniz Fatima^*, Y. Kelly Wu^*, and Ersin Uzun

2026

Conditionally accepted at CHI 2026
Understanding and Empowering Intelligence Analysts: User-Centered Design for Deepfake Detection Tools

Y. Kelly Wu, Saniat Javid Sohrawardi, Candice R. Gerstner, and Matthew Wright

In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan, 2025

Abs DOI

Intelligence analysts must quickly and accurately examine and report on information in multiple modalities, including video, audio, and images. With the rise of Generative AI and deepfakes, analysts face unprecedented challenges, and require effective, reliable, and explainable media detection and analysis tools. This work explores analysts’ requirements for deepfake detection tools and explainability features. From a study of 30 practitioners from the United States Intelligence Community, we identified the need for a comprehensive and explainable solution that incorporates a wide variety of methods and supports the production of intelligence reports. In response, we propose a design for an analyst-centered tool, and introduce a digital media forensics ontology to support analysts’ interactions with the tool and understanding of its results. We conducted a study grounded in work-related tasks as an initial evaluation of this approach, and report on its potential to assist analysts and areas for improvement in future work.
Dungeons & Deepfakes: Using scenario-based role-play to study journalists’ behavior towards using AI-based verification tools for video content

Saniat Javid Sohrawardi, Y. Kelly Wu, Andrea Hickerson, and Matthew Wright

In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, 2024

Abs DOI

The evolving landscape of manipulated media, including the threat of deepfakes, has made information verification a daunting challenge for journalists. Technologists have developed tools to detect deepfakes, but these tools can sometimes yield inaccurate results, raising concerns about inadvertently disseminating manipulated content as authentic news. This study examines the impact of unreliable deepfake detection tools on information verification. We conducted role-playing exercises with 24 US journalists, immersing them in complex breaking-news scenarios where determining authenticity was challenging. Through these exercises, we explored questions regarding journalists’ investigative processes, use of a deepfake detection tool, and decisions on when and what to publish. Our findings reveal that journalists are diligent in verifying information, but sometimes rely too heavily on results from deepfake detection tools. We argue for more cautious release of such tools, accompanied by proper training for users to mitigate the risk of unintentionally propagating manipulated content as real news.