Deep neural networks (DNNs) have no doubt brought great successes to a wide range of applications in computer vision, computational linguistics and AI. However, foundational principles underlying the DNNs’ success and their resilience to adversarial attacks are still largely missing. Interpreting and theorizing the internal mechanisms of DNNs becomes a compelling yet controversial topic. The statistical methods and rule-based methods for network interpretation have much to offer in semantically disentangling inference patterns inside DNNs and quantitatively explaining the decisions made by DNNs. Rethinking DNNs explicitly toward building explainable systems from scratch is another interesting topic, including new neural architectures, new parameter estimation methods, new training protocols, and new interpretability-sensitive loss functions.

This workshop aims to bring together researchers, engineers as well as industrial practitioners, who concern about interpretability, safety, and reliability of artificial intelligence. Joint force efforts along this direction are expected to open the black box of DNNs and, ultimately, to bridge the gap between connectionism and symbolism of AI research. The main theme of the workshop is therefore to build up consensus on a variety of topics including motivations, typical methodologies, prospective innovations of transparent and trustworthy AI. Research outcomes are also expected to have profound influences on critical industrial applications such as medical diagnosis, finance, and autonomous driving.

This workshop is a half-day event, which will include invited talks, spotlight and poster presentations of accepted papers.
We are calling for extended abstracts with 2–4 pages. Papers accepted by this workshop can be re-submitted to other conferences or journals.Please submit your papers to cvpr19xai@gmail.com

• Submission deadline: March, 30th, 2019
• Notification date: April, 11st, 2019
• Camera-ready deadline: April 18th, 2019