After the success of ML and AI-based approaches in outperforming traditional vision algorithms, recently a lot of research effort is dedicated to understanding of the limitations and the general behavior of AI methods in a broad range of computer vision applications. Specifically for a successful introduction of ML and AI in a wider range of products, safety is often a top priority. Being able to ensure safety of ML based computer vision is key to unlock its potential in a broad range of safety related applications and future products. In domains like automotive, aviation and the medical domain, it paves the way towards systems with a greater degree of autonomy and assistance for humans. 

Accepted papers will be published in IEEE Xplore!

What we aim to achieve

The workshop focuses on bringing together researchers, engineers, and practitioners from academia, industry, and government to exchange ideas, share their latest research, and discuss the latest trends and challenges in this field. The workshop also aims to foster collaboration between different stakeholders, including computer vision researchers, machine learning experts, robotics engineers and safety experts, to create a comprehensive framework for developing safe AI systems for all domains. 

Overall, the SAIAD workshop aims to advance the state-of-the-art in safe AI, address the most pressing challenges, and provide a platform for networking and knowledge sharing among the experts in this field.

This workshop will focus on safe artificial intelligence in a wide range of application domains of Computer Vision and Pattern Recognition. Compared to previous editions of SAIAD, which focused exclusively on the automotive sector, the organizers decided to broaden the scope of topics to include (non-exhaustive listing):

• Automated driving

• Applied robotics

• Aerospace

• Medical applications

• Rail traffic

This change has been reflected in the formation of the organizing committee as well as the proposed keynote speakers and other workshop content. We think that exchange across application domains of safe AI can stimulate the discovery of new approaches. We believe that regardless of the application domain, safety mechanisms for AI are implemented along the full development pipeline of safety-related AI-based systems: specification, data and model selection, training, evaluation / testing, monitoring and assurance argumentation.