Trustworthy Research for Understandable, Safe, Technology

The TRUST Lab at Duke University conducts research in applied AI explainability, technology evaluation, and adversarial alignment to ensure AI systems are transparent, safe, and beneficial for society.

Research Areas

Our interdisciplinary team tackles challenges in developing trustworthy technology.

Applied Explainability

Applying AI explainability methods to real-world problems.

Technology Evaluation

We focus both on the technical evaluation/benchmarking of AI systems and on the assessment of the societal impact of emerging technologies.

Adversarial Alignment

Using adversarial techniques to better explain AI systems.

Current Projects

Explainability in Conservation

Applied Explainability

In collaboration with:Duke Marine Robotics and Remote Sensing Lab (Nicholas School of the Environment)

Researchers:Jiayi Zhou, Gunel Aghakishiyeva

We are applying state of the art computer vision approaches and explainable machine learning techniques to support wildlife conservation efforts. This interdisciplinary project bridges machine learning with ecological science to create transparent decision-making tools.

Exploring Geolingual and Temporal Components of AI Embeddings

Applied Explainability

In collaboration with:The Emergence Lab (Computational Media, Arts, & Culture)

Researchers:Bochu Ding, Junyu Zhang, Vivienne Foley, Alexis Golart, Neha Shukla, James Sohigian

Funding:Duke Bass Connections

This project investigates how large embedding models encode geographical and temporal information, with implications for understanding cultural biases and historical shifts in AI systems.