Research Directions for Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems
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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.
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