Operational Explications of Classifier Predictions ((11) A05)

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We develop an explication framework for machine learning techniques with a particular focus on neural networks. As current classifiers are extremely brittle, we will work on provable robustness of the classifier decisions. For such robust classifiers we will provide operational explications presenting to the user how the input would have to change so that the classifier changes its decision, giving priority to slight changes in input. We will investigate efficient visualisation and presentation of the results, aiming at an interactive process exploring operational explications in the neighbourhood of the original input, supporting users in plausibilising the classifier decision, and supporting experts in machine learning when debugging.