Algorithmes explicables pour la détection d'anomalies et la prévision de séries chronologiques

Artificial intelligence has shown dominant performance in the data mining field, with applications across diverse domains, including critical ones such as medicine, finance, justice and so on. As a result, the explainability of black-box models is becoming moreand more demanding. We focus on two specific applications: anomaly detection and time series forecasting, and present XTREK and ADAPATCH for each task, respectively. XTREK is an unsupervised tree-based approach for explainable anomaly detection, which maximizes Kendall's τ between the anomaly scores of the source anomaly detector and those of XTREK. The tree produced by our algorithm is relatively small in size, therebyboasting the renowned off-the-shelf transparency and explainability of tree-based approaches. Moreover, its explanations are sample-based. In particular, the anomalyscores are computed to be the inverse of the size of the corresponding leaf, thereby providing meaningful explanations when comparing examples with different anomaly scores. XTREK can also be used as an in-model approach, which is capable of providing concise explanations for its own decisions. ADAPATCH is a post-hoc saliency map-based approach for explainable time series forecasting, which provides local visual explanationswith the help of perturbation-based gradient descent. With a differential encoding module in the mask of input, a more intuitive and higher-level semantic explanation can be provided. Both methods are model-agnostic, which means the architecture of the black-box model can be hidden from the users. They provide accurate and simple explanations and their accuracy are validated by extensive experiments.