SVEA: A Small-Scale Benchmark for Validating the Usability of Post-Hoc Explainable AI Solutions in Image and Signal Recognition

Abstract

Novel solutions in the area of Explainable AI (XAI) have made a significant breakthrough in increasing the trust of end-users in Machine Learning (ML) models. However, validating the performance of these solutions remains a challenging task. In this work, we focus on evaluating the methods that attribute a model’s decision to their input features. The prior metrics on this topic fail to consider multiple properties that a usable explainability solution should satisfy. Also, conducting experiments to assess the concreteness of the explanations provided by these solutions in large-scale datasets consumes excessive time and resources. To overcome these shortcomings, we propose the Small-scale Visual Explanation Analysis (SVEA) benchmark, which comprises the recent minimal MNIST-1D dataset. Our proposed benchmarking tool aids the practitioners and researchers to perform experiments on the Explainable AI methods without the need to access expensive computational devices. Furthermore, we offer a framework to evaluate various characteristics of the state-of-the-art XAI methods and include several widely used interpretability solutions in the SVEA benchmark to perform a thorough analysis of their completeness and understandability. The results obtained from our proposed evaluation metric suggest that specific approaches lack the ability to transfer the chosen model’s understanding to a second interpretable model by the explanations generated. The users can replicate our experiments within few minutes before working extensively on other larger datasets, thereby saving a lot of experimental time and effort.