Daniel Pham

Papers

Total Citations

H-Index

About

Daniel Pham’s research lies at the critical intersection of human-computer interaction and artificial intelligence, with a focused emphasis on trust, transparency, and autonomy in AI-driven systems. His most cited work, “A Case Study of Human-AI Interactions Using Transparent AI-Driven Autonomous Systems for Improved Human-AI Trust Factors” (2022), examines how explainable and transparent AI can foster greater trust between humans and autonomous technologies, particularly in high-stakes environments like defense and espionage. By studying real-world interactions with AI-based drones and ground robots used for terrain navigation and mapping, Pham identifies key design principles that enhance operator confidence and system reliability. Though his citation count is still growing—with this paper garnering 5 citations—his contributions are timely and impactful, addressing a core challenge in deploying autonomous systems where human oversight remains essential. Pham’s work is notable for bridging theoretical frameworks of trust with practical case studies, offering actionable insights for engineers and policymakers. As AI becomes increasingly embedded in critical operations, his research provides a foundational understanding of how transparency can mitigate risks and improve human-AI collaboration.