Abstract CounterFair is a group counterfactual search algorithm that detects and minimizes biases among sensitive groups and identifies relevant subgroups inside these sensitive groups based on shared counterfactual instances. We investigate the latter capability, analyzing the found subgroups from the perspective of fairness based on counterfactual reasoning, in order to evaluate whether they present different biases with respect to each other and to the sensitive feature groups they belong to. We perform these measurements on the subgroups extracted by CounterFair over six binary classification datasets, providing figures and their respective analysis on the presence of bias.

Time series analysis has been actively explored for different machine learning tasks, such as classification and forecasting, with the best-performing methods being based on complex deep learning architectures with limited interpretability and explainability. Explainability is mainly obtained by post-hoc analysis of a complex neural network. In contrast, inter-pretability is obtained by creating the most important human-understandable features further fed into a linear model. In this tutorial, we will introduce current trends and state-of-the-art algorithms, time series classification, and forecasting methods that are interpretable-by-design and/or explainable.

Counterfactual explanations can be used as a means to explain a models decision process and to provide recommendations to users on how to improve their current status. The difficulty to apply these counterfactual recommendations from the users perspective, also known as burden, may be used to assess the models algorithmic fairness and to provide fair recommendations among different sensitive feature groups. We propose a novel model-agnostic, mathematical programming-based, group counterfactual algorithm that can: (1) detect biases via group counterfactual burden, (2) produce fair recommendations among sensitive groups and (3) identify relevant subgroups of instances through shared counterfactuals. We analyze these capabilities from the perspective of recourse fairness, and empirically compare our proposed method with the state-of-the-art algorithms for group counterfactual generation in order to assess the bias identification and the capabilities in group counterfactual effectiveness and burden minimization.

Abstract Multivariate time series classification has become popular due to its prevalence in many real-world applications. However, most state-of-the-art focuses on improving classification performance, with the best-performing models typically opaque. Interpretable multivariate time series classifiers have been recently introduced, but none can maintain sufficient levels of efficiency and effectiveness together with interpretability. We introduce , a novel algorithm for effective and efficient interpretable multivariate time series classification. employs temporal abstraction and temporal relations of event intervals to create interpretable features across multiple time series dimensions. In our experimental evaluation on the UEA multivariate time series datasets, achieves comparable effectiveness to state-of-the-art non-interpretable multivariate classifiers while being faster than all interpretable multivariate classifiers. We also demonstrate that is more robust to missing values and inter-dimensional orders, compared to its interpretable competitors.

Most real-world datasets have a heterogeneous feature space composed of binary, categorical, ordinal, and continuous features. However, the currently available local surrogate explainability algorithms do not consider this aspect, generating infeasible neighborhood centers which may provide erroneous explanations. To overcome this issue, we propose ORANGE, a local surrogate explainability algorithm that generates highaccuracy and high-fidelity explanations in heterogeneous spaces. ORANGE has three main components: (1) it searches for the closest feasible counterfactual point to a given instance of interest by considering feasible values in the features to ensure that the explanation is built around the closest feasible instance and not any, potentially non-existent instance in space; (2) it generates a set of neighboring points around this close feasible point based on the correlations among features to ensure that the relationship among features is preserved inside the neighborhood; and (3) the generated instances are weighted, firstly based on their distance to the decision boundary, and secondly based on the disagreement between the predicted labels of the global model and a surrogate model trained on the neighborhood. Our extensive experiments on synthetic and public datasets show that the performance achieved by ORANGE is best-in-class in both explanation accuracy and fidelity.

Mining frequent patterns of event intervals from a large collection of interval sequences is a problem that appears in several application domains. In this paper, we propose Z-Miner, a novel algorithm for solving this problem that addresses the deficiencies of existing competitors by employing two novel data structures: Z-Table, a hierarchical hash-based data structure for time-efficient candidate generation and support count, and Z-Arrangement, a data structure for efficient memory consumption. The proposed algorithm is able to handle patterns with repetitions of the same event label, allowing for gap and error tolerance constraints, as well as keeping track of the exact occurrences of the extracted frequent patterns. Our experimental evaluation on eight real-world and six synthetic datasets demonstrates the superiority of Z-Miner against four state-of-the-art competitors in terms of runtime efficiency and memory footprint.