Proceedings of the Robust Artificial Intelligence System Assurance (RAISA) Workshop 2022

TL;DR

This paper discusses the RAISA workshop's focus on system-level robustness assurance for AI, emphasizing development, deployment, and human-machine teaming, addressing complex lifecycle and ethical considerations.

Contribution

It introduces a comprehensive approach to robustness assurance at the system architecture level, integrating fairness, privacy, and explainability considerations.

Findings

Emphasizes system-level robustness over individual models

Highlights importance of lifecycle and operational context

Addresses integration of ethical principles in robustness

Abstract

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.