Building trust in autonomous systems with an AI framework for privacy, safety, and reliability in data, software, and robotics

In this research, we propose an innovative framework designed to enhance privacy, safety, and reliability in data science, software development, and AI-empowered robotics, with a primary focus on building trust in autonomous systems. Our approach initially emphasizes the application of deep learning techniques to recover potential data values while bolstering data protection through privacy-preserving methods such as Differential Privacy. This is applied to heterogeneous data sources, including Census Demographics, OpenData 311 Calls, Neighborhood Crime, and LandBank data. As a result, we have achieved high accuracy in recovering missing and invalid data, while successfully preserving overall data distribution and ensuring privacy. For the second objective, our focus shifts to improving software system security through anonymous data collection tools. These tools are further enhanced with explainable and predictable measures of trust in human-machine collaboration, ensuring that these advanced processes remain transparent, accessible, and interpretable for all users, thereby increasing their overall trustworthiness. Within autonomous systems, robotics has been considered the most complex due to its continuous integration of data, software, and hardware. Because of this, privacy, safety, and reliability are the ultimate factors in deciding its trustworthiness as it involves not only a real-time decision-making process but also quite often heavy human-robot collaboration. As an integral concept of AI-empowered robotics, digital twins revolutionize the collaboration between virtual and real robotic systems, alongside reducing risks in reality and fostering human-robot interactions. Besides achieving state-of-the-art results in Object Goal Navigation, our approach emphasizes the use of digital twins, reinforcement learning, and imitation learning in other domains such as healthcare, surgery, and emergency handling, highlighting the importance of safety and reliability in the field of AI-empowered robotics. To gain trust between humans and autonomous systems, we subsequently deploy the diversely trained models in mixed-reality environments (AR/VR/XR), allowing experts to engage in an immersive and interactive experience where they can closely evaluate the robots' behaviors and decision-making processes in realistic scenarios.

Name thesis:degree_name

Ph.D. (Doctor of Philosophy)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Computer Science (UMKC)

Grantor

University of Missouri--Kansas City

Year dc:date.issued

2024

Author dc:creator

• Ho, Duy Hoang

Advisors dc:contributor.advisor

• Lee, Yugyung, 1960-
• Beard, Cory

Handle dc:identifier.uri

https://hdl.handle.net/10355/105801

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/105801