A Modular and Composable Approach to Develop Trusted Artificial Intelligence
Michael Austin Langford, Betty H. C. Cheng · 2022
Trustworthy artificial intelligence (Trusted AI) is of utmost importance when learning-enabled components (LECs) are used in autonomous, safety-critical systems. When reliant on deep learning, these systems need to address the reliability, robustness, and interpretability of learning models. In addition to developing specific strategies to address each of these concerns, appropriate software architectures are needed to coordinate LECs and ensure they deliver acceptable behavior under uncertain conditions. This work proposes a model-driven framework of loosely-coupled modular services designed to monitor and control LECs with respect to Trusted AI assurance concerns. The proposed framework is composable, deploying independent services to improve the resilience and robustness of AI systems. The overarching objective of this framework is to support software engineering principles focusing on modularity, composability, and reusability in order to facilitate development and maintenance tasks, while also increasing stakeholder confidence in Trusted AI systems. To demonstrate this framework, it has been implemented to manage the operation of an autonomous rover’s vision-based LEC while exposed to uncertain environmental conditions.