A ModelAgnostic Approach for Explaining the Predictions on Clustered Data Machine learning models especially deep neural network models have shown great potential in making decisions when analyzing clustered or longitudinal data. However, lack of model transparency is a major concern in risk sensitive domains such as social science and medical diagnosis. Despite the early success of explaining machine learning models, there is a lack of explanation methods that can be applied to any predictors on clustered data since most of the existing models assume that all observations are independent of each other. In this paper, we address this deficiency and propose to use a linear mixed model to mimic the local behavior of any complex model on clustered data, which can also improve the fidelity of the explanation method to the complex models. We apply our method to explain several models including a deep neural network model on two tasks including movie recommendation and medical record diagnosis. Experiment results show that our model outperforms the baseline models on several metrics such as fidelity and exactness.

Agnostic Local Explanation for Time Series Classification Recent advances in Machine Learning (such as Deep Learning) have brought tremendous gains in classification accuracy. However, these approaches build complex nonlinear models, making the resulting predictions difficult to interpret for humans. The field of model interpretability has therefore recently emerged, aiming to address this issue by designing methods to explain a posteriori the predictions of complex learners. Interpretability frameworks such as LIME and SHAP have been proposed for tabular, image and text data. Nowadays, with the advent of the Internet of Things and of pervasive monitoring, timeseries have become ubiquitous and their classification is a crucial task in many application domains. Like in other data domains, stateoftheart timeseries classifiers rely on complex models and typically do not provide intuitive and easily interpretable outputs, yet no interpretability framework had so far been proposed for this type of data. In this paper, we propose the first agnostic Local Explainer For TIme Series classificaTion (LEFTIST). LEFTIST provides explanations for predictions made by any time series classifier. Our thorough experiments on synthetic and realworld datasets show that the explanations provided by LEFTIST are at once faithful to the classification model and understandable by human users.