  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 Large Language Models (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.
