Post-hoc interpretability approaches have been proven to be powerful tools to generate explanations for the predictions made by a trained blackbox model. However, they create the risk of having explanations that are a result of some artifacts learned by the model instead of actual knowledge from the data. This paper focuses on the case of counterfactual explanations and asks whether the generated instances can be justified, i.e. continuously connected to some ground-truth data. We evaluate the risk of generating unjustified counterfactual examples by investigating the local neighborhoods of instances whose predictions are to be explained and show that this risk is quite high for several datasets. Furthermore, we show that most state of the art approaches do not differentiate justified from unjustified counterfactual examples, leading to less useful explanations.

In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, neither on the classifier itself, nor on the processed data (neither the training nor the test data). It proposes an instance-based approach whose principle consists in determining the minimal changes needed to alter a prediction: given a data point whose classification must be explained, the proposed method consists in identifying a close neighbour classified differently, where the closeness definition integrates a sparsity constraint. This principle is implemented using observation generation in the Growing Spheres algorithm. Experimental results on two datasets illustrate the relevance of the proposed approach that can be used to gain knowledge about the classifier.

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Time series shapelets proposes an approach to extract subsequences most suitable to discriminate time series belonging to distinct classes.Computational complexity is the major issue with shapelets: the time required to identify interesting subsequences can be intractable for large cases. In fact, it is required to evaluate all the subsequences of all the time series of the training dataset. In the literature, improvements have been proposed to accelerate the process, but few provide a solution that dramatically reduces the time required to find a solution.We propose a random-based approach that reduces the time necessary to find a solution, in our experimentation until 3 orders of magnitude compared to the original method.Based on extensive experimentations on several data sets from the literature, we show that even with a few time available, random-shapelet algorithm is able to find very competitive shapelets.

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Interpretable surrogates of black-box predictors trained on high-dimensional tabular datasets can struggle to generate comprehensible explanations in the presence of correlated variables. We propose a model-agnostic interpretable surrogate that provides global and local explanations of black-box classifiers to address this issue. We introduce the idea of concepts as intuitive groupings of variables that are either defined by a domain expert or automatically discovered using correlation coefficients. Concepts are embedded in a surrogate decision tree to enhance its comprehensibility. First experiments on FRED-MD, a macroeconomic database with 134 variables, show improvement in humaninterpretability while accuracy and fidelity of the surrogate model are preserved.