Context: Autonomous Systems (ASs) are becoming increasingly pervasive in today's society. One reason lies in the emergence of sophisticated Artificial Intelligence (AI) solutions that boost the ability of ASs to self-adapt in increasingly complex and dynamic environments. Companies dealing with AI models in ASs face several problems, such as users' lack of trust in adverse or unknown conditions, and gaps between systems engineering and AI model development and evolution in a continuously changing operational environment. Objective: This vision paper aims to close the gap between the development and operation of trustworthy AI-based ASs by defining a process that coordinates both activities. Method: We synthesize the main challenges of AI-based ASs in industrial settings. To overcome such challenges, we propose a novel, holistic DevOps approach and reflect on the research efforts required to put it into practice. Results: The approach sets up five critical research directions: (a) a trustworthiness score to monitor operational AI-based ASs and identify self-adaptation needs in critical situations; (b) an integrated agile process for the development and continuous evolution of AI models; (c) an infrastructure for gathering key feedback required to address the trustworthiness of AI models at operation time; (d) continuous and seamless deployment of different context-specific instances of AI models in a distributed setting of ASs; and (e) a holistic and effective DevOps-based lifecycle for AI-based ASs. Conclusions: An approach supporting the continuous delivery of evolving AI models and their operation in ASs under adverse conditions would support companies in increasing users' trust in their products.

中文翻译：

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在可信自治系统中开发和运行人工智能模型的研究方向

背景：自治系统 (AS) 在当今社会变得越来越普遍。原因之一在于复杂的人工智能 (AI) 解决方案的出现，这些解决方案提高了 AS 在日益复杂和动态环境中的自适应能力。在 AS 中处理 AI 模型的公司面临着几个问题，例如用户对不利或未知条件缺乏信任，以及在不断变化的操作环境中系统工程与 AI 模型开发和演进之间的差距。目标：本愿景文件旨在通过定义一个协调这两项活动的流程来缩小可信赖的基于 AI 的 AS 的开发和运营之间的差距。方法：我们综合了工业环境中基于 AI 的 AS 的主要挑战。为了克服这些挑战，我们提出了一部小说，整体 DevOps 方法并反思将其付诸实践所需的研究工作。结果：该方法建立了五个关键研究方向：(a) 可信度评分，用于监控基于 AI 的操作性 AS 并确定危急情况下的自适应需求；(b) 用于人工智能模型开发和持续演进的集成敏捷过程；(c) 用于收集解决人工智能模型在运行时的可信度所需的关键反馈的基础设施；(d) 在分布式 AS 环境中持续无缝地部署 AI 模型的不同上下文特定实例；(e) 基于 AI 的 AS 的整体且有效的基于 DevOps 的生命周期。结论：