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.

翻译：在涉及人机交互的环境中部署自主智能体日益引发安全关切。因此，理解事件背后的具体情境变得至关重要，这要求开发能够向非专业用户解释其行为的能力。此类解释对于提升可信度与安全性具有关键作用，可作为预防故障、错误及误解的前置措施。此外，它们有助于改善沟通机制，弥合智能体与用户之间的认知鸿沟，从而提升交互效能。本研究提出一种为基于ROS的移动机器人实现的可问责性与可解释性架构。该解决方案包含两个核心组件：首先，采用具备防篡改特性的类黑箱单元提供可问责性保障，其防篡改特性通过区块链技术实现；其次，设计专门组件负责利用大语言模型对前述黑箱中存储的数据生成自然语言解释。研究通过在三种不同场景（均涉及自主智能体导航功能）中对解决方案的性能进行评估，包括对可问责性与可解释性指标的全面检验。实验结果表明，本方法能有效利用机器人行动的可问责数据生成连贯、准确且易于理解的解释，即使在现实场景中面临自主智能体固有挑战的情况下仍保持良好性能。