Shao, XiaotingRienstra, TjitzeThimm, MatthiasKersting, Kristian2021-05-042021-05-0420202020http://dx.doi.org/10.1007/s13222-020-00351-xhttps://dl.gi.de/handle/20.500.12116/36405Machine learning and argumentation can potentially greatly benefit from each other. Combining deep classifiers with knowledge expressed in the form of rules and constraints allows one to leverage different forms of abstractions within argumentation mining. Argumentation for machine learning can yield argumentation-based learning methods where the machine and the user argue about the learned model with the common goal of providing results of maximum utility to the user. Unfortunately, both directions are currently rather challenging. For instance, combining deep neural models with logic typically only yields deterministic results, while combining probabilistic models with logic often results in intractable inference. Therefore, we review a novel deep but tractable model for conditional probability distributions that can harness the expressive power of universal function approximators such as neural networks while still maintaining a wide range of tractable inference routines. While this new model has shown appealing performance in classification tasks, humans cannot easily understand the reasons for its decision. Therefore, we also review our recent efforts on how to “argue” with deep models. On synthetic and real data we illustrate how “arguing” with a deep model about its explanations can actually help to revise the model, if it is right for the wrong reasons.Argumentation-based MLDeep Density EstimationExplainable AIInfluence FunctionInteractive MLProbabilistic CircuitsText/Journal Article10.1007/s13222-020-00351-x1610-1995