488 research outputs found
Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation
Trust and credibility in machine learning models are bolstered by the ability of a model to explain its decisions. While explainability of deep learning models is a well-known challenge, a further challenge is clarity of the explanation itself for relevant stakeholders of the model. Layer-wise Relevance Propagation (LRP), an established explainability technique developed for deep models in computer vision, provides intuitive human-readable heat maps of input images. We present the novel application of LRP with tabular datasets containing mixed data (categorical and numerical) using a deep neural network (1D-CNN), for Credit Card Fraud detection and Telecom Customer Churn prediction use cases. We show how LRP is more effective than traditional explainability concepts of Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) for explainability. This effectiveness is both local to a sample level and holistic over the whole testing set. We also discuss the significant computational time advantage of LRP (1–2 s) over LIME (22 s) and SHAP (108 s) on the same laptop, and thus its potential for real time application scenarios. In addition, our validation of LRP has highlighted features for enhancing model performance, thus opening up a new area of research of using XAI as an approach for feature subset selection
Explainability in Graph Neural Networks: A Taxonomic Survey
Deep learning methods are achieving ever-increasing performance on many
artificial intelligence tasks. A major limitation of deep models is that they
are not amenable to interpretability. This limitation can be circumvented by
developing post hoc techniques to explain the predictions, giving rise to the
area of explainability. Recently, explainability of deep models on images and
texts has achieved significant progress. In the area of graph data, graph
neural networks (GNNs) and their explainability are experiencing rapid
developments. However, there is neither a unified treatment of GNN
explainability methods, nor a standard benchmark and testbed for evaluations.
In this survey, we provide a unified and taxonomic view of current GNN
explainability methods. Our unified and taxonomic treatments of this subject
shed lights on the commonalities and differences of existing methods and set
the stage for further methodological developments. To facilitate evaluations,
we generate a set of benchmark graph datasets specifically for GNN
explainability. We summarize current datasets and metrics for evaluating GNN
explainability. Altogether, this work provides a unified methodological
treatment of GNN explainability and a standardized testbed for evaluations
Methods for Interpreting and Understanding Deep Neural Networks
This paper provides an entry point to the problem of interpreting a deep
neural network model and explaining its predictions. It is based on a tutorial
given at ICASSP 2017. It introduces some recently proposed techniques of
interpretation, along with theory, tricks and recommendations, to make most
efficient use of these techniques on real data. It also discusses a number of
practical applications.Comment: 14 pages, 10 figure
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