Developing a new approach for machine learning explainability combining local and global model-agnostic approaches

The last couple of past decades have seen a new flourishing season for the Artificial Intelligence, in particular for Machine Learning (ML). This is reflected in the great number of fields that are employing ML solutions to overcome a broad spectrum of problems. However, most of the last employed ML models have a black-box behavior. This means that given a certain input, we are not able to understand why one of these models produced a certain output or made a certain decision. Most of the time, we are not interested in knowing what and how the model is thinking, but if we think of a model which makes extremely critical decisions or takes decisions that have a heavy result on people’s lives, in these cases explainability is a duty. A great variety of techniques to perform global or local explanations are available. One of the most widespread is Local Interpretable Model-Agnostic Explanations (LIME), which creates a local linear model in the proximity of an input to understand in which way each feature contributes to the final output. However, LIME is not immune from instability problems and sometimes to incoherent predictions. Furthermore, as a local explainability technique, LIME needs to be performed for each different input that we want to explain. In this work, we have been inspired by the LIME approach for linear models to craft a novel technique. In combination with the Model-based Recursive Partitioning (MOB), a brand-new score function to assess the quality of a partition and the usage of Sobol quasi-Montecarlo sampling, we developed a new global model-agnostic explainability technique we called Global-Lime. Global-Lime is capable of giving a global understanding of the original ML model, through an ensemble of spatially not overlapped hyperplanes, plus a local explanation for a certain output considering only the corresponding linear approximation. The idea is to train the black-box model and then supply along with it its explainable version.