Vancouver, 18-22/06/2023.
iToBoS project was presented in the Safe Artificial Intelligence for All Domains (SAIAD) workshop, that took place at the CVPR2023 conference on computer vision, held in Vancouver, Canada, on June 18, 2023.
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.
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.
Find here the three posters through wich the iToBoS project was presented:
- Optimizing Explanations by Network Canonization and Hyperparameter Search.
- Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations.
- Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations.
Find more in SAIAD 2023.