We propose a novel eXplainable AI algorithm to compute faithful, easy-to-understand, and complete global decision rules from local explanations for tabular data by combining XAI methods with closed frequent itemset mining. Our method can be used with any local explainer that indicates which dimensions are important for a given sample for a given black-box decision. This property allows our algorithm to choose among different local explainers, addressing the disagreement problem, \ie the observation that no single explanation method consistently outperforms others across models and datasets. Unlike usual experimental methodology, our evaluation also accounts for the Rashomon effect in model explainability. To this end, we demonstrate the robustness of our approach in finding suitable rules for nearly all of the 700 black-box models we considered across 14 benchmark datasets. The results also show that our method exhibits improved runtime, high precision and F1-score while generating compact and complete rules.

While instance-level explanation of GNN is a well-studied problem with plenty of approaches being developed, providing a global explanation for the behaviour of a GNN is much less explored, despite its potential in interpretability and debugging. Existing solutions either simply list local explanations for a given class, or generate a synthetic prototypical graph with maximal score for a given class, completely missing any combinatorial aspect that the GNN could have learned. In this work, we propose GLGExplainer (Global Logic-based GNN Explainer), the first Global Explainer capable of generating explanations as arbitrary Boolean combinations of learned graphical concepts. GLGExplainer is a fully differentiable architecture that takes local explanations as inputs and combines them into a logic formula over graphical concepts, represented as clusters of local explanations. Contrary to existing solutions, GLGExplainer provides accurate and human-interpretable global explanations that are perfectly aligned with ground-truth explanations (on synthetic data) or match existing domain knowledge (on real-world data). Extracted formulas are faithful to the model predictions, to the point of providing insights into some occasionally incorrect rules learned by the model, making GLGExplainer a promising diagnostic tool for learned GNNs. Graph Neural Networks (GNNs) have become increasingly popular for predictive tasks on graph structured data. However, as many other deep learning models, their inner working remains a black box. The ability to understand the reason for a certain prediction represents a critical requirement for any decision-critical application, thus representing a big issue for the transition of such algorithms from benchmarks to real-world critical applications. Over the last years, many works proposed Local Explainers (Ying et al., 2019; Luo et al., 2020; Yuan et al., 2021; Vu & Thai, 2020; Shan et al., 2021; Pope et al., 2019; Magister et al., 2021) to explain the decision process of a GNN in terms of factual explanations, often represented as subgraphs for each sample in the dataset.

Global Forecasting Models (GFM) that are trained across a set of multiple time series have shown superior results in many forecasting competitions and real-world applications compared with univariate forecasting approaches. One aspect of the popularity of statistical forecasting models such as ETS and ARIMA is their relative simplicity and interpretability (in terms of relevant lags, trend, seasonality, and others), while GFMs typically lack interpretability, especially towards particular time series. This reduces the trust and confidence of the stakeholders when making decisions based on the forecasts without being able to understand the predictions. To mitigate this problem, in this work, we propose a novel local model-agnostic interpretability approach to explain the forecasts from GFMs. We train simpler univariate surrogate models that are considered interpretable (e.g., ETS) on the predictions of the GFM on samples within a neighbourhood that we obtain through bootstrapping or straightforwardly as the one-step-ahead global black-box model forecasts of the time series which needs to be explained. After, we evaluate the explanations for the forecasts of the global models in both qualitative and quantitative aspects such as accuracy, fidelity, stability and comprehensibility, and are able to show the benefits of our approach.

In precision medicine, machine learning techniques have been commonly proposed to aid physicians in early screening of chronic diseases. Many of these diseases become more difficult to treat as they progress, so accurate early screening is critical to ensure resources are directed towards the most effective treatment plan [Pagan, 2012]. Since the final treatment decision must inevitably be made by a doctor, these screening procedures should be interpretable such that a clinician can explain the decision-making process to patients for informed consent. However, the types of models that achieve the highest level of accuracy given early screening data tend to be extremely complex, meaning that even machine learning experts have difficulties explaining why certain predictions are made, leading many to describe them as "black box" [Breiman, 2001]. In this paper, we bridge this gap by providing a novel approach for explaining black box model predictions which can give high fidelity explanations with lower model complexity. In particular we will focus on early screening of Parkinson's Disease (PD). PD is a complicated neurodegenerative disorder that affects the central nervous system and specifically the motor control of individuals [mjf, 2019]. This disorder is estimated to affect 930,000 individuals in the US by 2020, and is more prevalent in the geriatric population affecting more then 1% of the population over the age of 60 and 5% of the population over age 85 [Findley, 2007, Kowal et al., 2013, Rossi et al., 2018]. These statistics and other recent studies on Parkinson's epidemiology indicate that as the population ages, the prevalence of PD is expected to grow to over 1.2 million by 2030 in the US alone, increasing the total economic burden of the disorder to approximately $26 billion USD [Kowal et al., 2013, Rossi et al., 2018].