Explainable and interpretable machine learning models and algorithms are important topics which have received growing attention from research, application and administration. Many advanced visual artificial intelligence systems are often perceived as black-boxes. Researchers would like to be able to interpret what the AI model has learned in order to identify biases and failure models and improve models. Many government agencies pay special attention to the topic. In May 2018, EU enacted the General Data Protection Regulation (GDPR) which mandates a right to explanation from machine learning model. An USA agency, DARPA, launched an Explainable Artificial Intelligence program in 2017. The ministry of science and ICT (MSIT) of South Korea established an Explainable Artificial Intelligence Center.

Recently, several models and algorithms are introduced to explain or interpret the decision of complex artificial intelligence system. Explainable Artificial Intelligence systems can now explain the decisions of autonomous system such as self-driving cars and game agents trained by deep reinforcement learning. Saliency maps built by attribution methods such as network gradients, DeConvNet, Layer-wise Relevance Propagation, PatternAttribution and RISE can identify relevant inputs upon the decisions of classification or regression tasks. Bayesian model composition methods can learn automated decomposition of input data into a composition of explainable base models in human pose estimation. Model agnostic methods such as LIME (Local interpretable model-agnostic explanations) and SHAP make complex deep learning models by providing importance of input features. Network Dissection and GAN Dissection provide human-friendly interpretations of internal nodes in deep neural network and deep generative models.

The present workshop aims to overview recent advances in explainable/interpretable artificial intelligence and establish new theoretical foundations of interpreting and understanding visual artificial intelligence models including deep neural networks. We will discuss the future research directions and applications of explainable visual artificial intelligence.

This workshop has interest including but not limited to the following topics.

- Explaining the decision of visual deep learning models

- Interpretable deep learning models

- Machine learning/deep learning models which generates human-friendly explanations

- Bayesian model composition/decomposition methods

- Model-agnostic machine learning explainable models

- Evaluation of explainable AI models

- Causal analysis of complex AI/ML systems

- Practical applications of explainable AI