Machine Learning on Big Data has gained an enormous popularity during recent years. In the vast majority of cases, the machine learning software for a particular data set is constructed as a neural network. However, this approach is in need of methods for explainable artificial intelligence, as most of the applied models are opaque and operate as a black-box. Other possible issues are the reliability, robustness and fairness of the machine learning software. These issues could be addressed with formal methods from automated deduction. 

The purpose of this workshop is to bring together researchers who work in machine learning and automated deduction. The targets of the workshop will be: to apply automated deduction methods to machine learning, to produce objects recognizable by automated deduction tools by machine learning methods, to enhance explainable artificial intelligence coupled with machine learning by automated deduction tools, and to consider further developments in the intersection of both research areas.