Proceedings of the Robust Artificial Intelligence System Assurance (RAISA) Workshop 2022
This work tackles the complex problem of robust AI system assurance for developers and operators, but it is incremental as it builds on existing research by shifting focus to system-level approaches.
The paper addresses the challenge of ensuring robustness in AI systems across their entire life cycle, focusing on system architecture and human-machine teaming rather than individual models, and highlights the need to integrate robustness with fairness, privacy, and explainability.
The Robust Artificial Intelligence System Assurance (RAISA) workshop will focus on research, development and application of robust artificial intelligence (AI) and machine learning (ML) systems. Rather than studying robustness with respect to particular ML algorithms, our approach will be to explore robustness assurance at the system architecture level, during both development and deployment, and within the human-machine teaming context. While the research community is converging on robust solutions for individual AI models in specific scenarios, the problem of evaluating and assuring the robustness of an AI system across its entire life cycle is much more complex. Moreover, the operational context in which AI systems are deployed necessitates consideration of robustness and its relation to principles of fairness, privacy, and explainability.