Enhancing Trust in Autonomous Agents: An Architecture for Accountability and Explainability through Blockchain and Large Language Models (2403.09567v3)

Abstract: The deployment of autonomous agents in environments involving human interaction has increasingly raised security concerns. Consequently, understanding the circumstances behind an event becomes critical, requiring the development of capabilities to justify their behaviors to non-expert users. Such explanations are essential in enhancing trustworthiness and safety, acting as a preventive measure against failures, errors, and misunderstandings. Additionally, they contribute to improving communication, bridging the gap between the agent and the user, thereby improving the effectiveness of their interactions. This work presents an accountability and explainability architecture implemented for ROS-based mobile robots. The proposed solution consists of two main components. Firstly, a black box-like element to provide accountability, featuring anti-tampering properties achieved through blockchain technology. Secondly, a component in charge of generating natural language explanations by harnessing the capabilities of LLMs over the data contained within the previously mentioned black box. The study evaluates the performance of our solution in three different scenarios, each involving autonomous agent navigation functionalities. This evaluation includes a thorough examination of accountability and explainability metrics, demonstrating the effectiveness of our approach in using accountable data from robot actions to obtain coherent, accurate and understandable explanations, even when facing challenges inherent in the use of autonomous agents in real-world scenarios.

• The paper introduces a dual-component system that uses blockchain for immutable event logging and LLMs for generating natural language explanations.
• The methodology rigorously evaluates the system through ROS-based mobile robot scenarios, confirming effective traceability and clear accountability.
• Experiments demonstrate that the integrated approach enhances trust in autonomous agents with minimal performance impact and resource overhead.

Enhancing Trust in Autonomous Agents through Blockchain and LLMs

Introduction

With autonomous agents increasingly coexisting with humans, the importance of ensuring their reliability and transparency has never been more apparent. Addressing security concerns integral to the deployment of these agents in human-centric environments necessitates a robust framework that not only guarantees the traceability of actions but also facilitates a deep understanding of these actions by non-expert users. In this regard, Laura Fernández-Becerra et al. propose an architecture leveraging blockchain technology for immutable logging and LLMs for natural language explanations. This combination aims to enhance both accountability and explainability of autonomous agents, focusing on ROS-based mobile robots.

System Architecture

The paper delineates a two-fold architecture: a black box-like component achieving accountability through blockchain technology, and a component responsible for generating natural language explanations utilizing LLMs. The black box component stores event logs with anti-tampering properties, ensuring data integrity and facilitating the assignment of responsibility in case of anomalies. Conversely, the explainability component leverages the data stored within the black box to generate understandable explanations for the robot's actions. An intermediate component processes and filters the raw data from the black box, refining the information for more effective explanation generation.

Experimental Setup and Evaluation

The evaluation of the proposed solution is rigorous and thorough, covering three different scenarios involving autonomous agent navigation functionalities. First, the accountability aspect was assessed across different methods of blockchain storage, examining the trade-offs between data granularity and system performance. The findings reveal a negligible impact on performance metrics such as CPU, RAM usage, and system load, validating the feasibility of integrating blockchain for this purpose. Subsequently, the effectiveness of the explainability component was explored through a multitude of questions, examining the coherence, accuracy, and helpfulness of the generated explanations.

Implications and Future Directions

The paper underscores the potential of utilizing blockchain and LLMs to address the dual challenge of accountability and explainability in autonomous agents. By providing a mechanism for immutable event logging and generating accessible explanations, this research paves the way for enhancing trust and safety in human-agent interactions. Future work might explore refining the retrieval mechanism of the RAG setup to improve answer relevancy and accuracy further. Additionally, extending the architecture's applicability beyond ROS-based mobile robots to other domains of autonomous agents represents an intriguing avenue for research.

Conclusion

This architecture represents a significant stride toward reconciling the opaqueness often associated with autonomous agents' functionalities. By ensuring actions are not only traceable but also interpretable to everyday users, the research contributes to demystifying the complexity of autonomous systems. The paper sets a precedent for leveraging advanced technologies like blockchain and LLMs in the field of robotics, highlighting their utility in fostering a safer and more transparent integration of autonomous agents into societal fabrics.