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). This workshop makes the first attempts towards bridging the gap of these two communities and aims to discuss principles of developing secure and safe ML systems. The workshop also focuses on how future practitioners should prepare themselves for reducing the risks of unintended behaviors of sophisticated ML models.

The workshop 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.