A Formal Introduction to Batch-Integrated Gradients for Temporal Explanations
Abstract: eXplainable Artificial Intelligence (XAI) is at the forefront of Artificial Intelligence research. Little attention, however, has been paid to the development of XAI methods for temporal data. Current state-of-the-art methods treat instances as independent and do not utilise the time dimension. Critical fields such as Healthcare and Finance often take a temporal form, leaving a prominent gap in XAI research. To this end, we propose the utilisation and optimisation of path based methods to use the temporal nature of data for explanations. In this work we (1) Extrapolate on a new technique for explainability forming a formal introduction, based on Integrated Gradients, a technique not designed for temporal data, (2) introduce new properties for time-based explainers and give an overview of the state-of-the-art methods and their adherence to these properties, (3) provide a theoretical and empirical analysis of path based methods, (4) demonstrate explanations on real world case studies. From this, we identify the proposed method best adheres to both the proposed properties and existing properties. Similarly, we demonstrate how the introduced method outperforms state-of the-art methods in the demonstrated areas
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