While machine learning (ML) models have achieved great success in many applications, concerns have been raised about their potential vulnerabilities and risks when applied to safety-critical applications. On the one hand, from the security perspective, studies have been conducted to explore worst-case attacks against ML models and therefore inspire both empirical and certifiable defense approaches. On the other hand, from the safety perspective, researchers have looked into safe constraints, which should be satisfied by safe AI systems (e.g., autonomous driving vehicles should not hit pedestrians).
In this workshop, we aim to bridge the gap of these two communities and discuss principles of developing secure and safe ML systems. We will bring together experts from machine learning, computer security, and AI safety communities. We attempt to highlight recent related work from different communities, clarify the foundations of secure and safe ML, and chart out important directions for future work and cross-community collaborations.
For more details please visit Security and Safety in Machine Learning Systems.